Building a Language Model for Local Coherence in Multi-document Summaries Using a Discourse-Enriched Entity-Based Model

Abstract

Abstract⎯ Local Coherence is a very important aspect in multidocument summarization, since good summaries not only condense the most relevant information, but also present it in a well-organized structure. One of the most investigated models for local coherence is the Entity-based model, which has been successfully used, once it facilitates the computational approach for coherence measurement. Particularly, this model was used for the evaluation of local coherence in multi-document summaries, achieving promising results. In order to improve the potential of the Entity-based model, we propose the creation of a language model for multi-document summaries that integrates the Entity-based model with discourse knowledge, mainly from Cross-document Structure Theory. Our results show that this type of information enriches the Entity-based Model by capturing other phenomena that are inherent to multi-document summaries, such as redundancy and complementarity, which improves the performance of the original model.

Análise retórica com base em grande quantidade de dados

“…Em especial, pela disponibilidade de quatro corpora com anotação RST, a saber, CSTNews (Cardoso et al , 2011a), CorpusTCC (Pardo & Nunes, 2004), Rhetalho (Pardo & Seno, 2005) e Summ-It (Collovini et al , 2007). Tais informações possibilitam o desenvolvimento de trabalhos baseados na informação discursiva e diversas pesquisas têm sido assim embasadas com o uso desses corpora (Cardoso et al , 2013;Jorge et al , 2014;Dias & Pardo, 2015;Cardoso et al , 2015;Cardoso & Pardo, 2016).…”