Multi-document summarization based on the Yago ontology

Baralis, Elena; Cagliero, Luca; Jabeen, Sadia; Fiori, Alessandro; Shah, Sayyar Ali

doi:10.1016/j.eswa.2013.06.047

Cited by 52 publications

(27 citation statements)

References 35 publications

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“…In addition to ROUGE and SIMetrix (supporting JensenShannon Divergence and Fraction of Topic Words) we are aware of other developments within the automatic summarisation evaluation literature [1,7,8,9,10]. However, we observe that ROUGE, often combined with manual evaluation, is still the accepted method for reporting summarisation results in the recent summarisation literature [2,15,29]. Hence, we focus on it and the model-free SIMetrix tool kit in this work.…”

Section: Automatic Evaluation Metricsmentioning

confidence: 93%

“…However, no such research has been conducted within the microblog domain. Instead, current literature on microblog summarisation reuse evaluation techniques and metrics shown to be effective for summarisation evaluation over newswire (most often ROUGE variants [2,15,29]). Hence, in this paper, we examine automatic summarisation evaluation metrics for microblog summarisation, to determine the metric(s) that most closely agrees with manual evaluation.…”

Section: Evaluation Of Summarisationmentioning

confidence: 99%

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On choosing an effective automatic evaluation metric for microblog summarisation

Mackie

McCreadie

Macdonald

et al. 2014

Proceedings of the 5th Information Interaction in Context Symposium

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Popular microblogging services, such as Twitter, are engaging millions of users who constantly post and share information about news and current events each day, resulting in millions of messages discussing what is happening in the world. To help users obtain an overview of microblog content relating to topics and events that they are interested in, classical summarisation techniques from the newswire domain have been successfully applied and extended for use on microblogs. However, much of the current literature on microblog summarisation assumes that the summarisation evaluation measures that have been shown to be effective on newswire, are still appropriate for evaluating microblog summarisation. Hence, in this paper, we aim to determine whether the traditional automatic newswire summarisation evaluation metrics generalise to the task of microblog summarisation. In particular, using three microblog summarisation datasets, we determine a ranking of summarisation systems under three automatic summarisation evaluation metrics from the literature. We then compare and contrast this ranking of systems produced under each metric to system rankings produced through a qualitative user evaluation, with the aim of determining which metric best simulates human summarisation preferences. Our results indicate that, for the automatic evaluation metrics we investigate, they do not always concur with each other. Further, we find that Fraction of Topic Words better agrees with what users tell us about the quality and effectiveness of microblog summaries than the ROUGE-1 measure that is most commonly reported in the literature.

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Section: Automatic Evaluation Metricsmentioning

confidence: 93%

Section: Evaluation Of Summarisationmentioning

confidence: 99%

On choosing an effective automatic evaluation metric for microblog summarisation

Mackie

McCreadie

Macdonald

et al. 2014

Proceedings of the 5th Information Interaction in Context Symposium

View full text Add to dashboard Cite

show abstract

“…Chen et al [49] introduce a user query-based text summarizer that uses the UMLS medical ontology to make a summary for medical text. Baralis et al [50] propose a Yago-based summarizer that leverages YAGO ontology [51] to identify key concepts in the documents. The concepts are evaluated and then used to select the most representative document sentences.…”

Section: Knowledge Bases and Automatic Summarizationmentioning

confidence: 99%

Text Summarization Techniques: A Brief Survey

Allahyari¹,

Pouriyeh²,

Assefi³

et al. 2017

ijacsa

401

175

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Abstract-In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

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“…Each component in W stores the term's weight of in normalized sentence ( ). The weights calculation is conducted using TF-ISF scheme in equation(1) and equation (2). Each term's weight then used to calculate the sentences similarity.…”

Section: Preprocessing Phasementioning

confidence: 99%

“…These algorithms include ontology-based, clustering, and heuristic approach. The example of document summarization method that uses ontology-based approach is the proposed method in [2]. It can perform multi-document summarization by utilizing Yago ontology to capture the intent and context of sentences in documents.…”

Section: Introductionmentioning

confidence: 99%

Untitled

Umam¹,

Putro²,

Pratamasunu³

et al. 2015

Jurnal Ilmu Komputer dan Informasi

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A great summarization on multi-document with similar topics can help users to get useful information. A good summary must have an extensive coverage, minimum redundancy (high diversity), and smooth connection among sentences (high coherence). Therefore, multi-document summarization that considers the coverage, diversity, and coherence of summary is needed. In this paper we propose a novel method on multi-document summarization that optimizes the coverage, diversity, and coherence among the summary's sentences simultaneously. It integrates self-adaptive differential evolution (SaDE) algorithm to solve the optimization problem. Sentences ordering algorithm based on topical closeness approach is performed in SaDE iterations to improve coherences among the summary's sentences. Experiments have been performed on Text Analysis Conference (TAC) 2008 data sets. The experimental results showed that the proposed method generates summaries with average coherence and ROUGE scores 29-41.2 times and 46.97-64.71% better than any other method that only consider coverage and diversity, respectively. Keywords: multi-document summarization, optimization, self-adaptive differential evolution, sentences ordering, topical closeness AbstrakPeringkasan yang baik terhadap dokumen-dokumen dengan topik yang seragam dapat membantu pembaca dalam memperoleh informasi secara cepat. Ringkasan yang baik merupakan ringkasan dengan cakupan pembahasan (coverage) yang luas dan dengan tingkat keberagaman (diversity) serta keterhubungan antarkalimat (coherence) yang tinggi. Oleh karena itu dibutuhkan metode peringkasan multi-dokumen yang mempertimbangkan tingkat coverage, diversity, dan coherence pada hasil ringkasan. Pada paper ini dikembangkan sebuah metode baru dalam peringkasan multi-dokumen dengan mengoptimasi tingkat coverage, diversity, dan coherence antarkalimat hasil ringkasan secara simultan. Optimasi hasil ringkasan dilakukan dengan menggunakan algoritma self-adaptive differential evolution (SaDE

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Multi-document summarization based on the Yago ontology

Cited by 52 publications

References 35 publications

On choosing an effective automatic evaluation metric for microblog summarisation

On choosing an effective automatic evaluation metric for microblog summarisation

Text Summarization Techniques: A Brief Survey

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