Source-linked AI summary
Single Document Extractive Summarization using Domination in Hypergraph
Aamir Miyajiwala, Aabha Pingle, Sheetal Sonawane, Surajit Kr. Nath
TL;DR
The paper addresses the challenge of capturing group and overlapping topical relations that pairwise graph summarizers miss in single-document extractive summarization. It builds a keyword- and entity-based sentence hypergraph and applies domination to select summary sentences. The method produces encouraging results comparable to TextRank and LexRank, particularly on the ESSG dataset.
Problem
Pairwise graph summarizers miss group relations among sentences, while prior hypergraph approaches inadequately represent overlapping themes in single-document summarization.
Method
The method creates a sentence hypergraph whose hyperedges are keywords or entities and uses a greedy algorithm to find a dominating set of summary sentences.
Results
The method gives encouraging results comparable to TextRank and LexRank, especially on the ESSG dataset.
Takeaways & Limitations
Domination in hypergraphs is presented as a good alternative to other graph-based methods for single-document extractive summarization.
Takeaways & Limitations
The approach has low ROUGE scores on DUC2002 and CNN/Daily Mail, which the paper attributes to their abstractive reference summaries.
Abstract
from arXiv · showhide
Automatic Text Summarization (ATS) in Natural Language Processing has been an important task in Information Retrieval. It compresses a document to create a summary that captures all the relevant and important information conveyed in the document. This study explores Hypergraph for extractive text summarization of single documents. Objective: This study explores a novel method of leveraging the property of domination in hypergraphs to generate an extractive summary and compare its performance with state of the art graph based methods. Method: Our work aims to generate an extractive summary by creating a sentence hypergraph where each sentence represents a node and the edge is a keyword or a named entity that contains the sentences in which it occurs. We generate a hypergraph where each edge is a keyword or an important topic and the nodes are sentences containing those keywords. Then we apply a greedy algorithm to find the dominating set of the hypergraph which will contain sentences that will form the extractive summary.
1. INTRODUCTION
The paper addresses the limitations of pairwise graph summarizers by using hypergraphs to represent group relations and overlapping topical coverage among sentences. It introduces domination-based sentence selection for unsupervised single-document extractive summarization.
- Hypergraphs address the limitation that conventional graph edges connect only sentence pairs and miss group relations.
- The proposed sentence hypergraph uses extracted keywords and entities as hyperedges grouping all sentences in which they occur.Because sentences may contain multiple keywords or entities, they can belong to multiple hyperedges and represent overlapping topics.
- Dominating-set selection extracts sentences containing important keywords while representing the document as a whole.
- The paper presents domination in hypergraphs as an early approach to unsupervised extractive summarization of a single document.
- The paper evaluates the approach against graph-based summarizers using experiments reported after the methodology and related-work sections.
2. LITERATURE REVIEW
The literature review contrasts graph-based sentence similarity models with hypergraph representations of topical groups. It identifies overlapping themes, computational cost, and the lack of prior domination-based single-document summarization as key context.
- A hypergraph extends a simple graph by allowing one edge to connect any number of vertices.
- The hypergraph is defined by vertex set X and edge set E, whose members are non-empty subsets of X.
- Graph-based extractive summarizers represent sentences as nodes and similarity links as edges, then select sentences through ranking or optimization.
- Hypergraph models represent groups of topically linked sentences, but prior methods were reported to have limited overlap, insufficient shared nodes, and high computational or storage costs.
- The paper reports that domination in hypergraphs had not previously been used to find a summary for a single document.
3. BASIC TERMINOLOGIES
This section introduces hypergraph adjacency and domination terminology used to formulate sentence selection. It defines stable and independent sets, dominating sets, and minimum domination concepts.
- Vertices are adjacent when they share an edge, while edges are adjacent when they share at least one vertex.
- A stable set contains no edge with more than one vertex, and its maximum size is the stability number α(H).
- A dominating set contains vertices such that every vertex outside it is adjacent to at least one selected vertex.
- A minimum dominating set has the least cardinality among dominating sets, and its size is the domination number γ(H).
- The paper states that all maximum stable sets are minimal dominating sets of the hypergraph.
4. METHODOLOGY
The methodology processes a document, extracts keywords and entities, constructs a sentence hypergraph, and selects sentences through domination. The system architecture organizes these steps into a document-summary generation pipeline.
- The methodology is presented through a system architecture for generating the document summary.
- The architecture connects these preprocessing and extraction stages to subsequent hypergraph construction and sentence selection.
- The system pipeline consists of document preprocessing, keyword and entity extraction, hypergraph creation, and domination-based sentence selection.
- Preprocessing removes stopwords, lemmatizes words with WordNet, removes special characters and punctuation, and segments sentences using regular expressions.
4.2. Keywords Extraction and Entity Recognition
The method extracts keywords and named entities to identify important information for single-document summarization. YAKE supplies document-level keywords, while spaCy NER complements it with entities that keyword extraction may miss.
- 4.2.1. YAKE: YAKE is used as an unsupervised, language- and domain-independent extractor of single-word keywords from each document.Its document-level operation suits independent single-document summarization without requiring a training corpus.
- 4.2. Keywords Extraction and Entity Recognition: Named Entity Recognition identifies people, organizations, and locations to capture relevant information.spaCy is used for the NER implementation.
- 4.2. Keywords Extraction and Entity Recognition: NER complements YAKE by recovering keywords that YAKE may not extract.
4.4. Sentence selection using Hypergraph.
The sentence-selection method treats summary generation as approximate hypergraph domination. A greedy algorithm selects sentences so that even singleton hyperedges preserve specific important content.
- 4.4. Sentence selection using Hypergraph: Because finding a dominating set is NP-Complete, the method uses a greedy algorithm to obtain an approximate solution.
- 4.4. Sentence selection using Hypergraph: Singleton hyperedges force their only sentence into the dominating set, helping retain specific information and maximize topical coverage.
- 4.4. Sentence selection using Hypergraph: The algorithm receives a keyword array and cleaned sentences, then groups sentences according to the keywords they contain.
9. end for
The supplied passages show termination and output stages of the processing procedures, including returning the keyword-to-sentence structure and iterating through selection logic.
- 9. end for: The keyword-to-sentence construction procedure ends by returning the dictionary W.
- 9. end for: The selection procedure takes a word-sentence dictionary as input and produces a dominating set.
- 9. end for: The algorithm marks mapped sentence entries as visited while processing the selection loops.
5. EXPERIMENTATION AND RESULTS
The study evaluates its hypergraph summarizer on DUC2002, ESSG, and CNN/Daily Mail using ROUGE and comparisons with TextRank and LexRank. The reported results are encouraging and comparable to graph-based baselines, especially on ESSG.
- 5. EXPERIMENTATION AND RESULTS: ROUGE-1, ROUGE-2, and ROUGE-L, together with precision and recall, are used to evaluate the generated summaries.
- 5. EXPERIMENTATION AND RESULTS: The study reports separate proposed-method score tables for DUC2002, the 100-document dataset, and CNN/Daily Mail.
6. CONCLUSION
The hypergraph-based method provides comparable performance to TextRank and LexRank on some datasets while avoiding sentence scoring and ranking computations.
- Comparable ROUGE scores to TextRank and LexRank were observed, especially on the ESSG dataset.The authors describe the method as a good alternative to other graph-based approaches.
- Low ROUGE scores on DUC 2002 and CNN/Daily_Mail were attributed to the abstractive nature of their reference summaries.Limited lexical overlap between reference and generated summaries can reduce ROUGE scores.
- The approach avoids sentence scoring and ranking computations, making it easier to implement and computationally effective.
- Co-reference resolution is identified as a potential way to further improve ROUGE scores.