Maria Lucía del Rosario Castro Jorge

Formalizing CST-based content selection operations

This paper presents the definition and formalization of content selection operations based on CST (Cross-document Structure Theory) for multidocument summarization purposes.

Content Selection Operators for Multidocument Summarization Based on Cross-Document Structure Theory

This paper aims at presenting an analysis of content selection techniques for multidocument summarization based on the multidocument discourse theory CST (Cross-document Structure Theory). We approach the task of content selection by using CST-based operators and focus specifically on redundancy treatment, which is an important and pervasive problem in multidocument summarization. Our experiments with Brazilian Portuguese news texts show that CST improves summaries quality by exploring relations among texts. Particularly, redundancy is reduced by identifying common information among texts, especially when compression rate is low.

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

Local Coherence is a very important aspect in multi-document 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 complementarily, which improves the performance of the original model.

Revisiting Cross-document Structure Theory for multi-document discourse parsing

Multi-document discourse parsing aims to automatically identify the relations among textual spans from different texts on the same topic. Recently, with the growing amount of information and the emergence of new technologies that deal with many sources of information, more precise and efficient parsing techniques are required. The most relevant theory to multi-document relationship, Cross-document Structure Theory (CST), has been used for parsing purposes before, though the results had not been satisfactory. CST has received many critics because of its subjectivity, which may lead to low annotation agreement and, consequently, to poor parsing performance. In this work, we propose a refinement of the original CST, which consists in (i) formalizing the relationship definitions, (ii) pruning and combining some relations based on their meaning, and (iii) organizing the relations in a hierarchical structure. The hypothesis for this refinement is that it will lead to better agreement in the annotation and consequently to better parsing results. For this aim, it was built an annotated corpus according to this refinement and it was observed an improvement in the annotation agreement. Based on this corpus, a parser was developed using machine learning techniques and hand-crafted rules. Specifically, hierarchical techniques were used to capture the hierarchical organization of the relations according to the proposed refinement of CST. These two approaches were used to identify the relations among texts spans and to generate multi-document annotation structure. Results outperformed other CST parsers, showing the adequacy of the proposed refinement in the theory.