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      Evaluating Text Summaries using Divergences of the Probability Distribution

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Abstract: This paper aims to show that generating and evaluating summaries are two linked but different tasks even when the same Divergence of the Probability Distribution (DPD) is used in both. This result allows the use of DPD functions for evaluating summaries automatically without references and also for generating summaries without falling into inconsistencies.

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          Most cited references21

          • Record: found
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          The Automatic Creation of Literature Abstracts

          H. P. Luhn (1958)
          Article 0 comments Cited 204 times – based on 0 reviews     Review now
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            An algorithm for suffix stripping

            M.F. Porter (1980)
            Article 0 comments Cited 174 times – based on 0 reviews     Review now
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              LexRank: Graph-based Lexical Centrality as Salience in Text Summarization

              G. Erkan, D. R. Radev (2004)
              We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.
              Article 0 comments Cited 143 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (publisher-id): cys
                Title: Computación y Sistemas
                Abbreviated Title: Comp. y Sist.
                Publisher: Centro de Investigación en computación, IPN (México, DF, Mexico )
                ISSN (Print): 1405-5546
                ISSN (Electronic): 2007-9737
                Publication date (Print and electronic): December 2020
                Volume: 24
                Issue: 4
                Pages: 1515-1526
                Affiliations
                [2] orgnameUniversidad Veracruzana orgdiv1Facultad de Matemáticas Mexico liquintana@ 123456uv.mx
                [3] Quebec orgnameÉcole Polytechnique de Montréal orgdiv1Département Génie Informatique et Génie Logiciel Canada ptoledo@ 123456uv.mx
                [1] Provence Alpes Cote d'Azur orgnameUniversité d'Avignon orgdiv1Laboratoire Informatique d’Avignon France juan-manuel.torres@ 123456univ-avignon.fr
                Article
                Publisher ID: S1405-55462020000401515 Publisher ID: S1405-5546(20)02400401515
                DOI: 10.13053/cys-24-4-3433
                SO-VID: eb2277ce-7ea6-472a-9ee7-d76a2acbabf0
                License:

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                Date accepted : 23 July 2020
                Date received : 22 June 2020
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 35, Pages: 12
                Product

                SciELO Mexico

                Categories
                Subject: Articles

                Keywords: Kullback-Leibler/Jensen-Shannon divergences,probability distribution,natural language processing,automatic text summarization
                Data availability:
                Keywords: Kullback-Leibler/Jensen-Shannon divergences, probability distribution, natural language processing, automatic text summarization

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