Query Focused Multi-document Summarization Based on Five-Layered Graph and Universal Paraphrastic Embeddings

Abstract

Query focused multi-document summarization is a process of automatic query biased text compression of a document set. Lately, the graph-based and ranking methods have been intensively attracted the researchers from extractive document summarization domain. The uniform sentence connecteness or non-uniform document-sentence connecteness, such as sentence similarity weighted by document importance, were the main features used by work to date. Contrary, in this paper we present a novel five-layered heterogeneous graph model. It emphasizes not only sentence and document level relations but also the influence of lower level relations (e.g. a part of sentence similarity) and higher level relations (i.e. query to sentences similarity). Based on this model, we developed an iterative sentence ranking algorithm, based on the existing well known PageRank algorithm. Moreover, for text similarity calculations we used universal paraphrase embeddings that outperform various strong baselines on many text similarity tasks and many domains. Experiments are conducted on the DUC 2005 data sets and the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) evaluation results demonstrate the advantages of the proposed approach.