Text Summarization through Entailment-based Minimum Vertex Cover

Gupta, Anand; Kaur, Manpreet; Mirkin, Shachar; Singh, Àmarjit; Goyal, Aseem

doi:10.3115/v1/s14-1010

Cited by 31 publications

(24 citation statements)

References 9 publications

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“…For example, question-answering systems may apply textual entailment techniques to identify an answer from previously stored databases [3], [24]. Textual entailment may also be used to enhance document summarization (e.g., being used to measure sentence connectivity [14], or as additional features to the summary generation [22]). Due to the recent increased interest on textual entailment research, publicly available benchmarks exist to evaluate such systems (e.g., [5], [30]).…”

Section: Open-domain Textual Entailmentmentioning

confidence: 99%

Combining Similarity and Transformer Methods for Case Law Entailment

Rabelo

Kim

Goebel

2019

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

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We tackle the complex problem of determining entailment relationships between case law documents, one of the tasks in the Competition on Legal Information Extraction and Entailment (COLIEE). With input of an entailed fragment from a case coupled with a candidate entailing paragraph from a noticed case, our approach relies on four main components: (1) extraction of similarity measures between the two pieces of text; (2) application of a transformer-based technique on the input text; (3) applying a threshold-based classier; and (4) post-processing the results considering the a priori probability determined by the data distribution on the training samples and combining the results of (1) and (2). Our experiments achieved an F-score of 0.70 on the ocial COLIEE test dataset, ranking rst among all competitors for that task in the 2019 competition.

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Section: Open-domain Textual Entailmentmentioning

confidence: 99%

Combining Similarity and Transformer Methods for Case Law Entailment

Rabelo

Kim

Goebel

2019

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

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“…is the popular term weighting function [26]. Thus, (6) can be redefined as follows: (7) Where w k denotes the k th word and is the inverse document frequency of each word .…”

Section: ) Semanticmentioning

confidence: 99%

Entailment and Spectral Clustering based Single and Multiple Document Summarization

Gupta¹,

Kaur²,

Bajaj³

et al. 2019

IJISA

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Text connectedness is an important feature for content selection in text summarization methods. Recently, Textual Entailment (TE) has been successfully employed to measure sentence connectedness in order to determine sentence salience in single document text summarization. In literature, Analog Textual Entailment and Spectral Clustering (ATESC) is one such method which has used TE to compute inter-sentence connectedness scores. These scores are used to compute salience of sentences and are further utilized by Spectral Clustering algorithm to create segments of sentences. Finally, the most salient sentences are extracted from the most salient segments for inclusion in the final summary. The method has shown good performance earlier. But the authors observe that TE has never been employed for the task of multi-document summarization. Therefore, this paper has proposed ATESC based new methods for the same task. The experiments conducted on DUC 2003 and 2004 datasets reveal that the notion of Textual Entailment along with Spectral Clustering algorithm proves to be an effective duo for redundancy removal and generating informative summaries in multi-document summarization. Moreover, the proposed methods have exhibited faster execution times.

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“…The evaluated system is just one implementation of this framework. Anand Gupta et al [32] introduced a novel method for single document text summarization. The model poses the text summarization task as an optimization problem, and attempts to solve it using Weighted Minimum Vertex Cover (WMVC), a graph-based algorithm.…”

Section: Graph-based Methodsmentioning

confidence: 99%

An Overview of Text Summarization

Rananavare¹,

Reddy²

2017

IJCA

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The amount of e-information available has increased greatly over the past few decades. As the vast amount of information is available for every theme on Internet, shortening the information in the form of summary would immensely benefit readers. Hence, the natural language processing research community is developing new methods for summarizing the text mechanically. Automatic text summarization system produces a summary, i.e. short length text that includes all the significant information for the article. This paper presents a comprehensive survey of contemporary text summarization of extractive and abstractive approaches.

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Text Summarization through Entailment-based Minimum Vertex Cover

Cited by 31 publications

References 9 publications

Combining Similarity and Transformer Methods for Case Law Entailment

Combining Similarity and Transformer Methods for Case Law Entailment

Entailment and Spectral Clustering based Single and Multiple Document Summarization

An Overview of Text Summarization

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