A Comparative Study of the Impact of Statistical and Semantic Features in the Framework of Extractive Text Summarization

Vodolazova, Tatiana; Lloret, Elena; Muñoz, Rafael; Palomar, Manuel

doi:10.1007/978-3-642-32790-2_37

Cited by 6 publications

(14 citation statements)

References 8 publications

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“…Lloret et al [7] reported on significant increase in ROUGE-1 values over the TF baseline when TE was used to identify the redundant information in a text. Similarly, Vodolazova et al [8] report that the best results are obtained when TE is included into the process of summary generation.…”

Section: Textual Entailmentmentioning

confidence: 95%

“…However, not all the systems equally benefit from including AR method. Vodolazova et al [8] integrated JavaRAP [9] to substitute anaphoric expressions by their antecedents and reported that AR per se decreases the quality of final summaries. But applying AR to the original text data prior to redundancy detection carried out using of textual entailment was shown to improve the quality of final summaries as compared to the system using AR or TE only.…”

Section: Anaphora Resolutionmentioning

confidence: 99%

“…Vodolazova et al [8] report on the poor results when the anaphora resolution is included. To verify the negative impact of AR on TS we integrated an additional coreference resolution system BART [10] .…”

Section: The Ar Modulementioning

confidence: 99%

“…Vodolazova et al [8] reported on the poor results when the stopwords filtering is combined with the inverse sentence and term frequencies, thus the introduced set of experiments does not include the ISF and ITF settings for the SSW option. …”

Section: System Settingsmentioning

confidence: 99%

“…The initial work [8] was focused on the impact of corpus-tailored stopwords on the process of TS and its integration with the abovementioned features. It was shown that some methods, for example, anaphora resolution implemented using JavaRAP [9] , need improvement.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The role of statistical and semantic features in single-document extractive summarization

Vodolazova

Lloret²,

Muñoz³

et al. 2013

AIR

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This paper reports on the further results of the ongoing research analyzing the impact of a range of commonly used statistical and semantic features in the context of extractive text summarization. The features experimented with include word frequency, inverse sentence and term frequencies, stopwords filtering, word senses, resolved anaphora and textual entailment. The obtained results demonstrate the relative importance of each feature and the limitations of the tools available. It has been shown that the inverse sentence frequency combined with the term frequency yields almost the same results as the latter combined with stopwords filtering that in its turn proved to be a highly competitive baseline. To improve the suboptimal results of anaphora resolution, the system was extended with the second anaphora resolution module. The present paper also describes the first attempts of the internal document data representation.

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Section: Textual Entailmentmentioning

confidence: 95%

Section: Anaphora Resolutionmentioning

confidence: 99%

Section: The Ar Modulementioning

confidence: 99%

Section: System Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The role of statistical and semantic features in single-document extractive summarization

Vodolazova

Lloret²,

Muñoz³

et al. 2013

AIR

Self Cite

View full text Add to dashboard Cite

show abstract

Text Summarization and Speech Synthesis for the Automated Generation of Personalized Audio Presentations

Lawless

Lavin

Bayomi

et al. 2015

Natural Language Processing and Information Systems

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Abstract. In today's fast-paced world, users face the challenge of having to consume a lot of content in a short time. This situation is exacerbated by the fact that content is scattered in a range of different languages and locations. This research addresses these challenges using a number of natural language processing techniques: adapting content using automatic text summarization; enhancing content accessibility through machine translation; and altering the delivery modality through speech synthesis. This paper introduces Lean-back Learning (LbL), an information system that delivers automatically generated audio presentations for consumption in a "lean-back" fashion, i.e. hands-busy, eyes-busy situations. These presentations are personalized and are generated using multilingual multi-document text summarization. The paper discusses the system's components and algorithms, in addition to initial system evaluations.

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Towards Evaluating the Impact of Anaphora Resolution on Text Summarisation from a Human Perspective

Bayomi

Levacher

Ghorab

et al. 2016

Natural Language Processing and Information Systems

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Abstract. Automatic Text Summarisation (TS) is the process of abstracting key content from information sources. Previous research attempted to combine diverse NLP techniques to improve the quality of the produced summaries. The study reported in this paper seeks to establish whether Anaphora Resolution (AR) can improve the quality of generated summaries, and to assess whether AR has the same impact on text from different subject domains. Summarisation evaluation is critical to the development of automatic summarisation systems. Previous studies have evaluated their summaries using automatic techniques. However, automatic techniques lack the ability to evaluate certain factors which are better quantified by human beings. In this paper the summaries are evaluated via human judgment, where the following factors are taken into consideration: informativeness, readability and understandability, conciseness, and the overall quality of the summary. Overall, the results of this study depict a pattern of slight but not significant increases in the quality of summaries produced using AR. At a subject domain level, however, the results demonstrate that the contribution of AR towards TS is domain dependent and for some domains it has a statistically significant impact on TS.

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A Comparative Study of the Impact of Statistical and Semantic Features in the Framework of Extractive Text Summarization

Cited by 6 publications

References 8 publications

The role of statistical and semantic features in single-document extractive summarization

The role of statistical and semantic features in single-document extractive summarization

Text Summarization and Speech Synthesis for the Automated Generation of Personalized Audio Presentations

Towards Evaluating the Impact of Anaphora Resolution on Text Summarisation from a Human Perspective

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