Multi-Document Summarization

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This project implements a professional-grade multi-document summarization system designed for SEO and digital strategy contexts. The system ingests multiple webpages, processes their content, and generates accurate, focused summaries that directly answer broad and complex user queries. Built using the Longformer transformer model, the pipeline handles real-world web content with varying quality, structure, and length.

The key innovation lies in clustering documents by topic similarity before summarization. This ensures that content with similar themes is processed together, preserving topical coherence and maximizing the relevance of generated outputs. Additionally, the system supports both query-focused and generic summarization, making it adaptable for different SEO use cases — such as competitive analysis, content consolidation, and search intent targeting.

The implementation includes robust mechanisms for content extraction, cleaning, topic grouping using sentence embeddings, and scalable summarization using long-document transformer models. Post-processing steps improve final readability and remove redundancies. The result is a system that transforms scattered content across URLs into clear, actionable insights — ready for client use in high-stakes SEO strategies.

Project Purpose

The purpose of this project is to enable SEO teams to generate high-quality, condensed summaries from multiple webpages that answer broad, intent-driven questions. In the SEO domain, content is often fragmented across several URLs, making it difficult to form a clear and unified understanding of a topic or address a specific search intent comprehensively.

This system is designed to address that gap by:

Aggregating information from several URLs on a similar topic.
Reducing noise and redundancy in long-form or repetitive web content.
Producing focused, query-relevant summaries that can serve strategic needs such as content audits, content gap analysis, and topic authority research.

By automating this process using a model that supports long-document summarization, the system reduces manual overhead, supports data-driven content decisions, and enhances SEO service offerings with deeper insights extracted from competitive or client-owned content.

Project’s Key Topics Explanation and Understanding

This section explains all key concepts mentioned in the project title and directly related areas that are crucial to understanding the goals, value, and execution of this real-world summarization project. The explanations are tailored for SEO professionals and clients who rely on advanced content intelligence to improve search performance, user engagement, and decision-making.

Definition Multi-document summarization (MDS) is the process of generating a single, coherent summary from multiple source documents that discuss a common or related topic. Rather than summarizing each document separately, MDS extracts and synthesizes key information across all of them to produce a unified response.

Why It Matters in SEO In SEO and digital marketing, businesses often operate in competitive spaces where valuable information is spread across multiple URLs — both their own and competitors’. SEO analysts need to process content from multiple sources to answer broad strategic questions like:

“What are the trends in competitor pricing strategies?”
“How are industry leaders positioning their service pages?”
“What topics do high-ranking pages focus on for a particular keyword cluster?”

Doing this manually is time-consuming and error-prone. MDS automates this, providing fast, coherent answers that combine signals from various URLs. This makes it easier to:

Benchmark competitors
Inform content writing strategies
Create content briefs or summaries
Generate domain insights for clients

How This Project Uses It The implemented system accepts multiple webpage URLs, extracts their cleaned content, clusters them based on topic similarity, and generates a final summary from each topic cluster — optionally guided by a user query. It simulates how an SEO analyst would read and consolidate content from various pages into a single actionable narrative.

Answering Broad Questions Accurately

Definition This refers to the model’s ability to not just summarize content but to focus the generated output around a broad user query (if provided). For example:

“What approaches are used in AI-driven content optimization?”

The model generates an answer from the combined context of multiple documents — even if the answer isn’t explicitly stated in any single source.

Why It Matters in SEO Broad questions are central to content research, competitive analysis, and strategic planning. Marketers often want distilled insights — not full articles — that can guide decisions or validate hypotheses.

How This Project Supports It The system supports optional query-aware summarization using prompt formatting. When a query is provided, the documents are prepended with an instruction-like prompt. This steers the summarization model (LED-base-16384) to generate a focused and contextually rich answer rather than a general summary.

This improves real-world usability, especially in tasks like:

Competitive research summaries
Executive brief generation
SEO audits or content gap analysis
Generating Q&A or FAQ sections for client sites

Query-aware Generation

Definition Query-aware generation is a technique where a summarization model is guided by a question or instruction prepended to the input text. Instead of merely summarizing, the model generates an answer grounded in the input context.

How It’s Implemented When a user provides a question (e.g., “What are the components of ThatWare’s AI-based recovery strategy?”), it is injected into the input as:

This style of prompting helps guide LED to align its output with the user’s intent.

SEO Use Cases

Creating focused briefs on industry trends
Auto-generating response sections for content hubs
Supporting automated FAQ synthesis
Generating client-specific strategic answers from competitor pages

Topic-coherent Clustering

Definition Before summarization, document blocks are grouped by semantic similarity using sentence embeddings. This ensures each group (cluster) contains content about the same underlying topic — improving the relevance and coherence of the summary generated from it.

How It’s Done in the Project

Sentence-transformers model is used to convert cleaned blocks into embeddings.
Agglomerative clustering groups similar blocks based on cosine similarity.
Clusters are then passed to the summarizer.

Why It’s Critical for Summarization Quality

Prevents mixing unrelated topics in a single summary.
Makes the generated output focused and internally consistent.
Enables more precise, structured summarization.

SEO Application

Helps analysts get summaries grouped by theme or keyword topic.
Supports organized answers to multifaceted queries.
Improves briefing material quality by keeping content tightly scoped.

Q&A Section to Understand Project Value and Importance

This section answers key questions that SEO professionals or clients may have regarding the purpose, benefits, and real-world applicability of the multi-document summarization system. The answers focus on how the project supports decision-making, automation, and strategic SEO insights.

What problem does this project solve for SEO professionals and clients?

SEO professionals often need to analyze large volumes of web content — including multiple pages from competitors, search results, or client-owned domains — to extract insights, content patterns, or strategic directions. Manually reviewing and summarizing this content is time-consuming and difficult to scale. This project automates that process by generating high-quality summaries from multiple documents, reducing hours of manual work to a few seconds.

How is this different from traditional summarization tools?

Most summarization tools are designed for single documents and often require manual trimming of content due to input limits. This system accepts multiple URLs, extracts their content in a structured way, groups similar content by topic, and generates coherent summaries — optionally tailored to a specific query. It is designed for scale, relevance, and practical decision support in SEO.

What kinds of questions or use cases does the system support?

The system supports both general summarization and targeted answering of broad strategic questions, such as:

What SEO techniques are competitors using?
How do multiple pages discuss a specific keyword or topic?
What is the common positioning of similar service pages?

This makes it suitable for generating content briefs, competitive summaries, FAQ content, SEO audit findings, and more.

What is the value for clients who receive the results?

Clients gain focused insights distilled from many sources, helping them:

Understand market or competitor positioning
Identify content opportunities or gaps
Take actions based on summarized evidence from real web content
Reduce reliance on manual research and interpretation

The output serves as a decision-support layer — directly actionable by both SEO teams and their clients.

Can the system handle queries specific business or domain?

Yes. The system supports query-aware summarization. When a broad business-related question is provided, the generated summary is aligned to answer that specific question using the information extracted from multiple webpages. This helps tailor outputs to client-specific contexts or campaigns.

Libraries Used

requests

The requests library is a standard and widely used HTTP client library in Python. It allows sending HTTP/1.1 requests with methods such as GET and POST. It simplifies the process of making web requests and handling responses, including headers, cookies, and timeout handling. It is known for its simplicity, reliability, and ease of use when dealing with network communication tasks in Python.

In this project, requests is used to fetch the raw HTML content of webpages provided via URLs. This step is the foundation for downstream document extraction and processing, as it allows accessing real-time content from multiple online sources. The library ensures that web content can be programmatically accessed and passed to the parsing layer.

BeautifulSoup and bs4.element.Comment

BeautifulSoup is a Python library used for parsing HTML and XML documents. It creates a parse tree that makes it easy to navigate, search, and modify the structure of web pages. It can handle poorly formatted markup and is typically used for web scraping tasks where HTML elements need to be filtered or extracted selectively.

In this project, BeautifulSoup is used as a fallback method for extracting visible content from HTML pages if the primary tool (trafilatura) fails. It also helps identify and remove irrelevant elements like scripts, styles, or comments using bs4.element.Comment. This fallback logic ensures robustness and resilience in content extraction, especially for non-standard or structurally inconsistent webpages.

charset_normalizer (from_bytes)

charset_normalizer is a character encoding detection library that serves as a drop-in replacement for chardet. It attempts to guess the correct encoding of byte streams and decode them into proper Unicode strings. This is crucial when scraping data from the web, where inconsistent or incorrectly labeled encodings can lead to decoding failures.

In this project, from_bytes is used to safely decode HTML responses obtained via requests. Some webpages return content that is not UTF-8 encoded, and using this library allows graceful handling of those cases, reducing the number of decoding-related failures during document retrieval.

trafilatura

trafilatura is a specialized Python library for web scraping and content extraction. It uses natural language processing and structural analysis to extract the main body text from a webpage, removing clutter like navigation bars, footers, and ads. It is optimized for semantic text extraction and supports multilingual content.

In this project, trafilatura serves as the primary tool for extracting clean, content-focused text from HTML pages. It ensures that only meaningful paragraphs and blocks are used for summarization, which directly improves the relevance and readability of the final summaries.

re (Regular Expressions)

The re module is Python’s built-in library for working with regular expressions. It allows for advanced text processing such as pattern matching, substitution, and string validation. It is especially useful in data preprocessing stages to clean or structure unstructured text data.

In this project, regular expressions are used to remove unwanted patterns from the extracted text, such as extra whitespace, HTML remnants, repeated punctuation, or non-standard characters. This step ensures that the content passed to the summarization model is as clean and informative as possible.

html and unicodedata

The html module provides tools for escaping and unescaping HTML entities, which is necessary when dealing with web content. unicodedata is a standard library for Unicode character database access, often used to normalize characters, remove diacritics, or standardize encoding.

These two libraries are used together in the preprocessing phase. html helps decode HTML entities (e.g., & → &) while unicodedata is used to normalize text for consistent formatting. This ensures semantic accuracy and uniformity across content blocks, particularly useful in NLP tasks where input clarity influences model performance.

sentence_transformers

sentence_transformers is a library built on top of Hugging Face Transformers and PyTorch that allows generating dense vector embeddings for sentences, paragraphs, or documents. It enables semantic similarity comparison, clustering, and ranking of text blocks.

In this project, it is used to create vector representations of each content block across all documents. These embeddings are the basis for grouping similar blocks using clustering techniques. This enables the system to identify and summarize coherent topics across multiple webpages.

sklearn.cluster.AgglomerativeClustering

AgglomerativeClustering is a hierarchical clustering algorithm from scikit-learn. It builds nested clusters by successively merging or splitting groups based on similarity. It does not require predefining the number of clusters in certain configurations and is well-suited for document-level or semantic clustering tasks.

In this project, it is used to group semantically similar content blocks into topic clusters. These clusters guide the summarization pipeline by ensuring that each summary corresponds to a well-defined, focused theme, thereby improving the clarity and utility of the output.

collections.defaultdict

The defaultdict is an extension of the regular Python dictionary that provides a default value for missing keys. This prevents runtime errors when accessing keys that may not have been initialized yet.

Within this project, defaultdict is used to organize content blocks by their assigned topic cluster. It simplifies the process of accumulating blocks for each group and ensures that the code remains concise and robust during batch processing.

numpy and cosine_similarity

numpy is the core scientific computing library in Python, used for array manipulation, numerical operations, and matrix algebra. cosine_similarity is a utility function from sklearn.metrics.pairwise used to calculate similarity scores between vectorized inputs.

These are used together to compute the pairwise similarity between block embeddings. cosine_similarity quantifies how close two content blocks are in meaning, while numpy manages the underlying data structures. The results are essential for determining semantic closeness during clustering.

typing module

The typing module allows the use of type hints and annotations in Python, improving code readability and facilitating better development practices. It supports declaring complex data structures like nested dictionaries, lists of tuples, and union types.

In this project, type annotations are used throughout the pipeline functions to declare the expected input and output structures clearly. This ensures better maintainability, easier debugging, and clearer documentation, especially important in multi-developer or client-facing environments.

transformers (from Hugging Face)

The transformers library by Hugging Face provides pre-trained models for a wide range of natural language tasks such as text classification, translation, question answering, and summarization. It offers seamless integration with PyTorch and TensorFlow and supports hundreds of pre-trained models.

Here, AutoTokenizer and AutoModelForSeq2SeqLM are used to load and apply the led-base-16384 model for long-document summarization. The library handles tokenization, model inference, and generation, enabling the system to create high-quality summaries that are semantically rich and grammatically sound.

torch

torch (PyTorch) is an open-source machine learning framework widely used for developing deep learning applications. It supports dynamic computation graphs, GPU acceleration, and modular model building.

In this project, PyTorch is used implicitly through the transformers and sentence_transformers libraries to run model inference. It enables the efficient execution of tokenization, embedding generation, and summarization on GPU when available, reducing latency and improving scalability.

nltk (Natural Language Toolkit)

nltk is a leading Python library for natural language processing, offering tools for tokenization, stemming, tagging, and syntactic parsing. It is often used in preprocessing pipelines for tasks involving text segmentation or transformation.

Here, nltk is used for sentence tokenization and stemming. These steps are useful for deduplicating similar content and improving the structure of inputs passed to the summarization model. It helps break down large blocks into finer segments, ensuring more precise and readable output.

Function extract_blocks

Summary

This function is responsible for extracting high-quality textual content blocks from a webpage. It uses trafilatura as the primary method for robust, content-aware extraction. If trafilatura fails, the function falls back to a custom BeautifulSoup-based extractor that filters noise like ads, navigation, and hidden elements. It returns cleaned and validated blocks containing a minimum number of words, with duplication and encoding errors handled gracefully.

Key Implementation Details

· Request Handling with Robust Headers and Validation

· The request uses a standard browser-like user-agent to avoid getting blocked. It raises exceptions for bad responses to stop further processing early for broken or inaccessible pages.

· HTML Content-Type Check

· Ensures that only HTML content is processed. Other types like PDF or images are skipped early.

· Encoding Resolution

· This block intelligently handles character encoding using declared encoding or a fallback using charset_normalizer. It avoids corrupt or misread text during parsing.

· Primary Extraction via trafilatura

downloaded = trafilatura.extract(html, include_comments=False, include_tables=False, no_fallback=True)

trafilatura is used as the first choice due to its structure-aware and noise-free content extraction. Extracted text is split into paragraphs and each paragraph is validated against a minimum word count before inclusion.

· Fallback to Custom Extraction via BeautifulSoup If trafilatura fails or returns empty, the function switches to a handcrafted content extraction logic:

soup = BeautifulSoup(html, “lxml”)

Tags that commonly contain scripts, ads, navigation, and styling (e.g., <script>, <style>, <footer>, etc.) are removed. Comments and hidden DOM elements are also stripped to clean up the noise.

· Block Collection from Visible Tags

Only textual content from visible and meaningful tags is selected. Each block is cleaned, normalized, and validated against a word count.
Heuristics for Block Quality

· Non-ASCII heavy blocks (often in corrupted formats or spam) are filtered out. Additionally, a hash digest is used to remove duplicate blocks.

All valid blocks are returned along with the original URL. This structure supports multi-URL processing and grouped result display.

Function preprocess_blocks

Summary

This function performs rigorous text cleaning and quality filtering on raw content blocks extracted from webpages. It prepares the text for downstream summarization by removing noisy artifacts such as URLs, boilerplate phrases, formatting symbols, and HTML encodings. The output is a list of uniformly cleaned blocks containing only meaningful content, suitable for accurate summarization and clustering.

Key Implementation Details

Boilerplate and Noise Detection Rules

To remove common web artifacts that dilute content quality or confuse summarization models, the function defines multiple patterns:

· Boilerplate Pattern

Captures frequently repeated phrases typically found in website footers, navigation, or CTAs (calls to action). These have no semantic value for summarization and are filtered out early.

· URL Pattern

url_pattern = re.compile(r’https?://\S+|www\.\S+’)

Used to strip out embedded links, which are irrelevant in summary-level content generation.

· List Formatting Patterns Removes numbering and bullets used in lists:

Bullet-style markers like -, •, *
Numbered items like 1., 2:, 3-
Roman numerals like II, IV.

These are stripped from the start of each line to clean sentence structure.

Text Normalization and Character Cleanup

The inner function clean_text(text: str) applies a series of transformations to sanitize the input string:

· HTML Unescaping Converts encoded characters like   or & to readable form using html.unescape().

· Unicode Normalization Applies “NFKC” form normalization to standardize special characters into consistent ASCII representations.

· Non-ASCII Removal Removes extended Unicode characters that might disrupt downstream tokenizers or embeddings:

re.sub(r”[^\x00-\x7F]+”, ” “, text)

· Substitutions Dictionary Applies common replacements:

““”” → ‘”‘, “‘’” → “‘”, “–—” → “-“, “\u00A0” → space, “\u200B” → ”

This standardizes stylistic variations that can lead to duplication or inconsistency.

· Spacing Normalization Collapses excess spaces and trims leading/trailing whitespace to ensure consistency.

Function filter_documents_by_query_similarity

Summary

This function filters the cleaned webpage content blocks based on their semantic similarity to a user-provided query. It ensures that only content relevant to the question or topic of interest is passed to the summarization stage. The filtering uses sentence-level embeddings and cosine similarity to measure contextual alignment. If no query is provided, all blocks are returned as-is.

Key Implementation Details

Query Validation and Early Return

The function first checks whether the user has provided a valid query. If the query is missing or empty, the function returns all blocks without filtering. This ensures fallback compatibility for non-query-driven summarization.

Embedding Generation Using SentenceTransformer

The query is encoded using the SentenceTransformer model, which transforms the string into a high-dimensional vector representing its semantic meaning.
All blocks are similarly converted to embeddings.
These embeddings allow a meaningful comparison between the user’s query and the content blocks, independent of keyword overlap.

Cosine Similarity Computation

similarities = cosine_similarity([query_embedding], block_embeddings)[0]

cosine_similarity from sklearn computes similarity scores between the query and each block.
A score near 1 means high semantic similarity, while values near 0 indicate irrelevance.

Threshold-Based Filtering

Only those blocks that meet or exceed the specified similarity_threshold are retained.
The threshold (default 0.3) balances relevance and coverage: higher values result in stricter filtering, while lower values allow broader inclusion.

Function load_cleaned_documents

This function performs full-cycle preprocessing of webpage content across a list of URLs. It handles webpage block extraction, text cleaning, and optional semantic filtering based on a user-provided query. The resulting output is a list of clean, high-quality content blocks ready for downstream summarization. Each block maintains a reference to its source URL for traceability.

This function represents the main data intake pipeline for the project. In real-world use, webpages often contain a mix of structured, semi-structured, and noisy content. By chaining together high-quality extraction, robust cleaning, and optional semantic filtering, this component guarantees that only clean, relevant, and query-aligned text enters the summarization process. This improves both the quality and trustworthiness of the generated summaries—essential in professional, client-facing SEO applications.

Function load_embedding_model

Summary

This function loads a sentence embedding model used to convert raw text into high-dimensional vector representations. These embeddings are essential for clustering similar content together and for filtering document blocks based on their semantic relevance to a given query.

Key Implementation Details

return SentenceTransformer(model_name)

The function uses the SentenceTransformer interface from the sentence-transformers library to load the specified model.
The default model used is all-mpnet-base-v2, a state-of-the-art transformer known for producing highly accurate sentence-level embeddings.
The loaded model can be directly used to compute embeddings for individual sentences, paragraphs, or full content blocks.

Sentence Embedding Model: all-mpnet-base-v2

Overview

The summarization pipeline uses the all-mpnet-base-v2 sentence embedding model from the sentence-transformers library. This model is a fine-tuned version of Microsoft’s MPNet language model, specifically optimized to produce meaningful vector representations of sentences and short texts. These representations, known as embeddings, allow for semantic similarity comparisons between different text units.

Model Architecture Components

The model internally consists of three core components:

Transformer Backbone The core of the model is a transformer architecture based on MPNet (MPNetModel). MPNet is a high-performance transformer that improves upon previous models like BERT and RoBERTa by combining masked language modeling with permuted language modeling. This enables it to better capture contextual relationships in text.
Pooling Layer After the transformer processes the input, the output embeddings from all tokens are pooled using a mean pooling strategy (pooling_mode_mean_tokens=True). This means the final sentence embedding is computed as the average of all token vectors.based tasks.
Normalization Layer The resulting vector is normalized to have unit length (Normalize()), which is crucial for consistent cosine similarity calculations. This ensures that similarity scores are meaningful and comparable across different input pairs.

Why This Model Was Chosen

High Performance: all-mpnet-base-v2 consistently ranks among the top performers on the Sentence Embedding Benchmark (SBERT), offering strong generalization across various semantic similarity tasks.
Efficiency: It provides a good balance between accuracy and computational efficiency, making it suitable for real-time or large-scale document clustering and filtering.
Compatibility: Fully integrated with the sentence-transformers library, enabling plug-and-play use in real-world pipelines like this summarization project.

Application in This Project

Content Clustering: Embeddings generated by this model are used to group similar blocks of content across webpages into semantically coherent clusters.
Query Filtering: The same embeddings enable intelligent filtering of irrelevant content, ensuring that only blocks aligned with the user’s query are retained before summarization.

This model forms the backbone of semantic understanding in the project, bridging the gap between raw unstructured text and structured, query-relevant summaries.

Function cluster_documents_by_topic

Summary

This function groups multiple webpages into topic-coherent clusters based on the semantic similarity of their content. Each cluster aggregates content from similar documents, forming a unified input for summarization. It ensures that the final summaries are not diluted across unrelated sources but instead reflect distinct topical themes, enhancing both focus and relevance.

Key Function Roles

Input: A flat list of cleaned blocks, each with text and its source url.
Output: A list of topic clusters. Each cluster is a dictionary with:
- source_urls: URLs grouped under the same topic.
- combined_input: A string of all cleaned texts from those URLs, concatenated.

Key Implementation Details

Embedding Each Block

block_texts = [b[‘text’] for b in preprocessed_blocks] block_embeddings = model.encode(block_texts, convert_to_tensor=False)

Each cleaned content block is transformed into a high-dimensional vector using the sentence embedding model.
These embeddings represent the semantic meaning of the text, which is crucial for comparing topic similarity across documents.
Grouping Blocks by URL

url_to_embeddings = defaultdict(list) url_to_texts = defaultdict(list)

Blocks are reorganized by their originating URL.
This allows the system to treat each full page as a unit, rather than mixing blocks from different sources prematurely.
Mean Pooling Across Blocks (Per URL)

url_embeddings = { url: np.mean(embs, axis=0) for url, embs in url_to_embeddings.items() }

A single embedding is computed for each webpage by averaging its block embeddings.
This enables document-level semantic comparisons in a compact form.
Fallback for Single Document

if len(urls) == 1: return [{…}]

If only one document exists, it returns that document as a single cluster, bypassing clustering logic.
Agglomerative Clustering

clustering = AgglomerativeClustering(…) cluster_ids = clustering.fit_predict(emb_matrix)

An unsupervised clustering algorithm groups documents based on the cosine distance between their mean embeddings.
The distance_threshold determines how close documents must be to form a cluster.
linkage=’average’ computes inter-cluster distance based on average linkage, balancing between strict and loose grouping.
Assembling Final Clusters

clusters[cluster_id][“source_urls”].append(url) clusters[cluster_id][“combined_input”] += …

Each cluster collects all its source URLs and concatenates their texts.
This consolidated cluster text becomes the input to the summarization model.

Function load_summarization_model

Summary

This function initializes a pre-trained long-document summarization model and tokenizer from the Hugging Face Transformers library. It prepares the model for inference on GPU or CPU, enabling the generation of summaries from large volumes of concatenated webpage text.

Key Implementation Details

Tokenizer Initialization

tokenizer = AutoTokenizer.from_pretrained(model_name)

Loads the tokenizer associated with the specified model.
Tokenizers are responsible for converting raw text into token IDs compatible with the model, including handling padding, truncation, and special tokens.
For long-input models like LED (allenai/led-base-16384), this tokenizer is specifically configured to handle very large context windows (up to 16,384 tokens).
Model Initialization

model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

Loads the encoder-decoder transformer model pre-trained for summarization.
The AutoModelForSeq2SeqLM class ensures compatibility with a wide range of models trained on summarization or generation tasks.
Device Management

device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”) model.to(device)

Automatically detects whether a GPU is available and moves the model accordingly.
This ensures optimal performance for summarizing large inputs, particularly when processing multiple document clusters.
Set Model to Evaluation Mode

model.eval()

Disables training-specific features like dropout, ensuring consistent results during inference.

Function Summarization Model: allenai/led-base-16384

Overview

The summarization engine powering this project is allenai/led-base-16384, a transformer-based model designed for processing exceptionally long documents. It is based on the Longformer Encoder-Decoder (LED) architecture, a specialized extension of the traditional transformer model that enables handling inputs up to 16,384 tokens—far beyond the 512-token limit of standard models like BERT or T5.

This capacity makes it ideal for summarizing large volumes of concatenated text from multiple webpages in a single pass, preserving more context, structure, and meaning in the generated output.

Core Innovations of the Model

Longformer Self-Attention

Traditional transformers scale quadratically with input length due to full self-attention. LED overcomes this limitation using:

Sliding Window Attention: Each token attends only to its local neighborhood.
Global Attention Tokens: A small subset of tokens (e.g., titles, sentence starts) can attend globally across the document.

This combination retains rich contextual understanding while reducing computational load from O(n²) to O(n).

Encoder-Decoder Architecture

Unlike Longformer (which is encoder-only), LED is a full encoder-decoder model:

The encoder processes the long input document with efficient self-attention.
The decoder generates summaries using autoregressive decoding, attending to encoder outputs.

This allows LED to produce high-quality abstractive summaries with long context memory.

Pretraining on Summarization Tasks

The LED model was pre-trained and fine-tuned on large-scale summarization datasets like arXiv and PubMed, which include full research papers—documents with similar length and structure to the concatenated content clusters in this project.

Model Capabilities for This Project

Input Length: Up to 16,384 tokens (words, punctuation, and symbols), allowing large sets of webpages to be summarized without truncation.
Output Style: Produces abstractive summaries—not just extracted sentences but new, coherent, human-like summaries that capture key ideas.
Relevance Retention: Maintains topic coherence over long text spans, reducing the risk of factual drift or missing key context.

Why LED Was Chosen

Real-World Fit: Ideal for use cases like SEO, where multiple webpages need to be synthesized into a cohesive answer or insight.
Context Preservation: Avoids chopping text into disconnected fragments, a common issue in other summarization models.
Stability: Despite the long input, LED provides robust performance with consistent quality when paired with proper preprocessing and clustering.

Function prepare_inputs_from_clusters()

Summary

This function converts clustered documents into clean, deduplicated text blocks optimized for long-document summarization. It ensures that input size stays within model constraints while maximizing relevance and uniqueness of content.

Function Purpose

Prepare Inputs per Cluster: Converts raw concatenated content into model-ready blocks while maintaining coherence per topic cluster.
Handle Token Budget: Avoids exceeding model input limits (e.g., 16,384 tokens for LED) by splitting large clusters into manageable segments.
Remove Redundant Sentences: Deduplicates overlapping or repetitive sentences to improve summary clarity and focus.

Key Implementation Details

Tokenization of Text by Sentence

sentences = sent_tokenize(combined_text)

Each cluster’s combined content is split into individual sentences for finer control over content selection and de-duplication.

Redundancy Filtering with Stemming

stem_key = ” “.join(ps.stem(w.lower()) for w in sent.split()[:6])

· A basic semantic deduplication mechanism is applied:

Sentences shorter than min_sentence_length words are skipped.
For the remaining sentences, the first 6 words are stemmed using PorterStemmer and used as a de-duplication key.
This prevents near-duplicate or copy-pasted sentences across pages from polluting the input with redundant information.

Chunking by Word Budget

if word_count + len(words) > max_words:

· Summarization models like LED have a strict token limit.

· Sentences are grouped into input blocks such that their total word count does not exceed max_words (default: 16,000).

· When the word limit is reached:

The current chunk is finalized and saved.
A new chunk is started.

· This enables dynamic splitting of large clusters while retaining contextual continuity.

Function summarize_text_block

Summary

This function generates either a generic or query-aware summary from a block of long-form input text using a pretrained transformer-based summarization model. It handles input formatting, token-level truncation, and summary generation with quality-control decoding parameters.

Function Purpose

Summarize Informational Blocks: Produces a concise and informative summary from a potentially large input text block.
Supports Query-Aware Generation: When a query is provided, the output is focused on answering that specific question.
Prevents Common Summary Issues: Enforces minimum and maximum lengths and avoids repetition or incoherence via decoding constraints.

Key Implementation Details

Prompt Construction

prompt = f”Query: {query}\n\nUse the following information to answer:\n{summary_text}”

If a query is provided, the input is restructured into a query-aware prompt, guiding the model to focus on answering it using the provided context.
Without a query, the raw text is summarized generically.

Tokenization with Truncation

tokenizer(prompt, truncation=True, max_length=max_input_length, …)

Ensures the prompt doesn’t exceed the model’s maximum input capacity (default: 4096 tokens).
Long documents are truncated intelligently at the token level, preserving relevant prefixes.

Summary Generation with Decoding Control

model.generate(…, max_length=…, min_length=…, no_repeat_ngram_size=…, repetition_penalty=…, …)

num_beams=4: Uses beam search for better summary quality.
no_repeat_ngram_size=3: Prevents repeating 3-word phrases to reduce redundancy.
repetition_penalty=1.3, length_penalty=1.1: Encourage informative, concise outputs.
early_stopping=True: Stops generation once the best candidate is found.

Postprocessing

tokenizer.decode(…, skip_special_tokens=True)

Converts the token IDs back into a clean, human-readable summary string.
Special tokens like <pad>, <s>, or </s> are removed to ensure clean output.

Function run_summarization

Summary

This function performs the end-to-end summarization process across all input blocks, generating clean summaries that are either general or question-specific. It loops through the prepared inputs, applies summarization per block, and stores each result with its traceable source URLs.

Function Purpose

Automates Summarization Across All Blocks: Applies summarization individually to each input document chunk.
Handles Both Query and Non-query Cases: Supports question-guided answer generation or general content summarization.
Preserves Source Attribution: Maintains a link between each summary and its original source URLs.

Key Implementation Details

Calls the Summarization Engine

summary = summarize_text_block(…)

· Delegates actual summarization logic to summarize_text_block() which handles:

Tokenization and truncation
Query-aware or generic prompting
Controlled decoding

· This separation ensures modularity and makes debugging or improvements easier.

Collects and Structures Output

For each summarized chunk:
- The clean output summary is saved alongside its associated URLs.
- This format ensures the client can trace each part of the summary back to its source, which is crucial for SEO, audits, and transparency.

Function clean_summary

Summary

This function takes the raw output summaries generated from multiple document chunks and transforms them into a clean, readable, and non-redundant final summary. It removes repetition, fixes formatting issues, and ensures proper sentence structure—making the final output client-presentable.

Key Implementation Details

Iterates Through Summaries

for url_summary in summaries: summary = url_summary.get(“summary”, “”)

Each item in the input is expected to be a dictionary containing a summary key.
Empty or malformed summaries are safely skipped.

Initial Clean-Up

summary = re.sub(…) # Normalize quotes, newlines, excessive spaces

· Fixes common formatting issues:

Replaces escaped characters.
Collapses excessive whitespace.
Removes non-alphanumeric characters at the beginning.

Grammar and Punctuation Fixes

summary = re.sub(r'([.,!?;:])(?=[^\s])’, r’\1 ‘, summary) summary = re.sub(r’\s+([.,!?;:])’, r’\1′, summary)

Ensures punctuation is followed by a space, and not preceded by excess space.
Improves the natural flow and readability of the text.

Removes Redundancies and Artifacts

summary = re.sub(r”\b(\w+)( \1\b)+”, r”\1″, …)

Collapses repeated words (e.g., “very very important” → “very important”).
Strips leftover prompt templates like Query:…Answer using….

Sentence-Level Tokenization and Filtering

sentences = sent_tokenize(summary)

· Breaks the summary into individual sentences.

· Filters out:

Short fragments (under 3 words).
Incomplete marketing phrases (e.g., ending with “website” or “tools”).

· Drops final sentence if it’s grammatically incomplete (e.g., lacks proper punctuation).

Function generate_summaries_by_clusters

Summary This function orchestrates the full multi-document summarization workflow. It starts with retrieving cleaned content from a set of URLs, groups related content by topic, prepares token-efficient inputs, and finally runs a query-aware summarization model to generate high-quality answers or summaries.

Function Purpose

End-to-End Orchestration: Manages every stage of the summarization pipeline—from raw URL input to final cleaned output.
Topic-Focused Summarization: Ensures content from different webpages is grouped by theme before summarization, improving coherence and specificity.
Query-Aware Answer Generation: Supports both question-guided answers and general-purpose summaries.
Final Output Optimization: Delivers a cleaned, non-redundant summary ready for downstream use.

Result Analysis and Explanation

This section presents an interpretation of the answer generated by our multi-document summarization model. The summary is based on content drawn from multiple service-specific webpages and generated in response to a focused query. The purpose of this analysis is to help website owners understand what the result reflects, how to use it, and what insights it provides into the source content.

Strategic Content Aggregation

The generated answer successfully integrates information from different service-related webpages into a single, cohesive summary. By combining content across pages, the result offers a unified overview of business strategies discussed throughout the site. This method is particularly useful when a client wants to understand the overarching positioning and communication across related services without manually reading through each individual page.

This allows stakeholders to gain a consolidated understanding of how multiple offerings relate to a common strategic theme—such as growth-driven SEO or performance-based marketing.

Marketing Message Reinforcement

The result emphasizes key marketing values that are consistently communicated across the selected webpages. Themes such as strategic investment, high-value returns, and scalable success are prominent in the summary. These reflect the brand’s messaging emphasis and the way it positions its services in alignment with client success outcomes.

This type of summarization helps reinforce the messaging consistency of service-related content and can serve as a reference for ensuring future content maintains alignment with these strategic priorities.

Query-Relevant Insight Extraction

The model-generated answer directly addresses the input query by drawing content that relates to business strategy in a digital service context. The answer demonstrates the system’s ability to locate and present content that aligns with a client’s informational need, even when the content is distributed across several documents.

Use this approach to explore how well their web content answers key questions their audience might have, enabling strategic adjustments to improve message clarity and coverage.

Content Style and Expression Reflection

The summarization output mirrors the tone and style used in the source materials, providing an authentic representation of how the content presents value. This can be helpful in reviewing whether the tone aligns with the brand’s voice and whether it communicates authority, clarity, and professionalism.

The ability to extract and reflect a consistent tone across multiple pages gives content teams an additional tool for auditing brand voice and content style cohesiveness.

Support for SEO and Content Planning

From an SEO and content planning standpoint, the summary highlights recurring concepts that appear across service pages. This can be useful in identifying strong thematic signals, assessing keyword concentration, and spotting areas of overlap or emphasis. The method offers a scalable way to audit content alignment and evaluate whether key messages are being effectively surfaced.

Use this to identify focus areas in their content that are already working well, while also planning future differentiation strategies across service offerings to maintain clarity and search value.

Q&A Section for Result Interpretation Actions

How does this summary help in understanding the combined SEO offerings from multiple service pages?

The summary provides a consolidated overview of the key themes and messaging found across multiple SEO service pages. By combining content from the Advanced SEO, Digital Marketing, and Managed SEO offerings, it highlights recurring strategies, language patterns, and focal points used throughout the site. This allows website owners to identify overlaps in service positioning, repeated promises or selling points, and potentially redundant messaging across pages. It also provides clarity on how the brand collectively presents its marketing strategy—helping to refine messaging for consistency and precision in future content development.

What insights can we gather about content quality and clarity from this result?

The generated output reveals areas where the original page content may be over-reliant on repetition or generalization. The repeated emphasis on terms like high-quality strategy, high-rise strategy, and performance improvement indicates a heavy use of persuasive phrases without always anchoring them in specific, actionable value. This helps website owners understand that while the messaging tone is assertive and confident, the clarity and uniqueness of propositions could be enhanced. These insights can guide future content audits to ensure more grounded, differentiated service descriptions that speak directly to business outcomes.

How should we act on these findings to improve content strategy or site performance?

Website owners can take several actionable steps based on the summarization output:

Refine content hierarchy: Consolidate similar messaging from multiple service pages into a single authoritative resource to reduce fragmentation.
Introduce clearer value differentiators: Ensure each service page has a distinct proposition to avoid overlapping descriptions that confuse visitors.
Improve semantic clarity: Replace abstract or overly generalized phrases with concrete, measurable outcomes (e.g., “Increase lead conversion by 25%” instead of “Ensure high ROI”).
Strengthen internal linking: Use insights from the summary to better interconnect related services, ensuring users can seamlessly explore complementary offerings.

Can this output inform our future SEO copywriting or content planning?

Yes. The summarization output offers a real-world reflection of what a user or search engine might extract as the essence of the combined service messaging. This is especially valuable for:

SEO Copy Audits: It highlights phrases and themes that occur frequently, suggesting which ideas might be overused and where diversification is needed.
Content Clustering Strategy: By observing how thematically similar content has been grouped and summarized, website owners can structure their content around well-defined topical clusters that support SEO goals.
Persona Alignment: The messaging tone can be matched or adjusted to better suit buyer personas, ensuring more persuasive and relatable copy across digital touchpoints.

How reliable is this summary as a foundation for broader marketing or SEO decisions?

While the summary is generated from high-quality, multi-page input using a powerful model, it is best used as a strategic aid rather than a standalone decision-making tool. It reliably identifies patterns, core language, and messaging trends, making it useful for:

Strategic reviews of positioning and tone.
Content consolidation planning.
Copywriting consistency checks.

However, it should be supplemented with manual content audits, SEO performance data, and user engagement metrics for a complete picture. Used this way, the summary becomes a strong support layer in a broader content and SEO decision-making framework.

Final Thoughts

This multi-document summarization project provides a structured, efficient way to extract the core messaging and themes across multiple service pages. By using advanced language models, we’ve been able to consolidate long-form content into clear, query-guided outputs that support both strategic insight and practical action.

The result helps clients quickly understand how their services are presented collectively, where overlaps may exist, and how the brand’s value propositions are communicated across pages. This serves as a foundation for improving content clarity, consistency, and SEO performance moving forward.

As part of an ongoing SEO and content optimization strategy, this approach can be applied to other sections of the website or competitive content—enabling faster analysis, better planning, and more informed decisions.

Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.

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