Source-linked AI summary
Scientific Paper Summarization Using Citation Summary Networks
Vahed Qazvinian, Dragomir R. Radev
TL;DR
Researchers face a large literature burden when moving into new fields, motivating systems that summarize scientific topics. This paper takes an initial step by summarizing individual articles through citation summaries and citation-network clustering, and reports higher average P scores for C-lexrank than C-RR and Lexrank on the evaluated problem. The method’s round-robin cluster selection can choose sentences without facts, sometimes lowering P scores.
Problem
Researchers need efficient ways to move into unfamiliar research areas because each field contains a vast scientific literature.
Method
The paper clusters citation-summary networks, whose nodes are citing sentences and whose strong edges indicate shared facts, to select sentences for an article summary.
Results
C-lexrank has a higher average pyramid score P than C-RR and Lexrank on the evaluated articles, while the clustering method outperforms Lexrank on this problem.
Takeaways & Limitations
Single-article summaries based on citation viewpoints and network analysis provide a framework for future research on whole-topic summarization.
Takeaways & Limitations
Round-robin selection from clusters does not guarantee fact-bearing sentences, which can sometimes produce low P scores.
Abstract
from arXiv · showhide
Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others' viewpoint of the target article's contributions and the study of its citation summary network using a clustering approach.
1 Introduction
The paper frames topic summarization as a progression from summarizing individual articles using citation summaries. It proposes selecting a shorter set of citing sentences through citation-summary network analysis, extending prior citation and scientific summarization work.
- Topic summarization can help researchers enter unfamiliar fields by revealing a field’s chronology and progress.
- Citation summaries collect sentences from other articles that cite a target article, providing information about its contributions from others’ viewpoints.
- Summarizing an entire topic may first be reduced to summarizing one article, then combining article summaries with citation-network knowledge.
- The paper’s main contribution is a framework that uses citation summaries and network analysis to summarize a single scientific article.
- Related Work: Prior work analyzed citation and collaboration networks, scientific article summaries, citation categories, survey generation, and citation-based search indexing.
- Related Work: Large-scale evidence found citation summaries more focused than abstracts and containing additional information absent from abstracts.
2 Data
The dataset is drawn from the ACL Anthology Network and organized into five topic clusters. The evaluation uses five selected articles from each cluster, with citation-summary size recorded for each paper.
- The ACL Anthology contains almost 11,000 computational-linguistics papers, and the AAN adds citation and author-collaboration network metadata.
- The full AAN citation network contains 8,898 nodes and 38,765 directed edges.
- Clusters: Five topic clusters were collected by matching a phrase in paper titles or content: Dependency Parsing, Phrase-Based Machine Translation, Text Summarization, Question Answering, and Textual Entailment.
- Clusters: Five articles were chosen from each cluster for evaluation.
- Clusters: Citation summary size is defined as the number of citation sentences that cite a paper.
3 Analysis
The analysis uses annotator-derived facts to evaluate how well sentence similarities identify shared contributions in citation summaries. These annotations support fact distributions and comparison of similarity measures.
- Fact Annotation: Annotators read citation summaries of 25 papers and extracted non-overlapping lists of perceived main contributions.They were instructed to focus on the citation summaries rather than prior topic knowledge.
- Fact Annotation: The union of shared and similar annotator facts was used to label which facts each citation-summary sentence contained.Facts appearing in only one annotator’s result were ignored.
- Fact Distribution: The fact-distribution matrix represents sentences as rows, facts as columns, and marks sentence-fact coverage with 1 values.The matrices were created from annotations and used in evaluation.
- Similarity Measures: Similarity should assign high values to sentence pairs sharing facts and low values to pairs without shared target-article contributions.The desired similarity structure is intended to produce network communities corresponding to facts.
- Similarity Measures: The evaluation ranks sentence-pair similarities and counts fact-sharing pairs among the top 100, 200, and 300 pairs.For P99-1065, 172 pairs shared facts among 2,862 total pairs; tested measures included cosine variants, generation probability, and string-edit distances.
4 Methodology
The methodology builds a citation summary network whose sentence similarities support graph clustering, then extracts representative sentences from the resulting communities. It compares cluster-based extraction with Random Summary and whole-network Lexrank baselines.
- Network-Based Clustering: A citation summary network is a complete undirected weighted graph whose nodes are citing sentences and whose edge weights represent sentence similarity.Similarities are designed so sentences sharing facts have strong edges, making clusters likely to indicate shared facts.
- Network-Based Clustering: Clustering assigns citation-summary sentences to communities, and purity measures the number of correctly assigned documents divided by N.The evaluation reports real facts, clusters, and purity for each article.
- Sentence Extraction: Cluster Round-Robin starts with the largest cluster and extracts sentences in within-cluster order until reaching the summary length limit |S|.Beginning with the largest community prioritizes facts appearing more frequently and therefore having higher weights.
- Sentence Extraction: Cluster Lexrank applies Lexrank within each cluster and selects sentences by decreasing cluster size and within-cluster salience.It takes the most salient sentence from each cluster, then proceeds to the second-most salient sentences until the length limit.
- Baseline Methods: The baselines are Mead’s Random Summary and Lexrank applied to the entire citation summary network.Whole-network Lexrank builds lexical sentence connections and uses a random walk to identify central summary sentences.
- Baseline Methods: Table 6 presents P99-1065 system summaries produced by C-RR and five-sentence C-Lexrank.The table compares the two cluster-based extraction methods’ resulting summaries.
5 Experimental Setup
The evaluation measures whether short summaries cover important facts from citation summaries, comparing system outputs with gold and random summaries. C-lexrank achieves a higher average pyramid score than C-RR and Lexrank, while C-RR can select sentences without facts.
- Evaluation Method: Pyramid evaluation scores summaries by the weighted facts they contain relative to an optimal summary.Facts appearing in more citation-summary sentences receive higher weights, and scores range from 0 to 1.
- Evaluation Method: Gold summaries select sentences covering new, highly weighted facts, whereas random summaries provide a weaker comparison baseline.The expected system behavior is to be closer to gold than to random summaries.
- Results and Discussion: C-lexrank has a higher average pyramid score than C-RR and Lexrank across the evaluated articles.
- Results and Discussion: C-RR does not guarantee fact-bearing sentence selection because round-robin extraction chooses sentences from clusters regardless of their factual content.Non-fact-bearing sentences can appear first in clusters and sometimes produce low pyramid scores.
6 Conclusion and Future Work
The paper selects a few important sentences from citation summaries using clustering, positioning single-article summarization as groundwork for future topic summarization. It reports that clustering outperforms Lexrank on this task and proposes reducing redundancy and improving coherence in future work.
- Conclusion: The method selects a few important sentences from citation summaries, whose experimental sizes range from a few sentences to a few hundred.The experiments select five sentences in each summary.
- Conclusion: Clustering forms communities in the citation-summary lexical network and extracts sentences from separate clusters.
- Conclusion: The clustering method outperforms Lexrank on this scientific-paper summarization problem.
- Future Work: Future work proposes MMR re-ranking to reduce redundancy and language generation to improve summary coherence.The longer-term goal is topic summarization ordered by chronology and topicality.