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SUBTOPIC‐BASED MULTIMODALITY RANKING FOR TOPIC‐FOCUSED MULTIDOCUMENT SUMMARIZATION
Author(s) -
Wan Xiaojun
Publication year - 2013
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2012.00435.x
Subject(s) - automatic summarization , computer science , sentence , ranking (information retrieval) , relevance (law) , natural language processing , information retrieval , set (abstract data type) , rank (graph theory) , benchmark (surveying) , artificial intelligence , task (project management) , modality (human–computer interaction) , modalities , mathematics , social science , management , geodesy , combinatorics , sociology , political science , law , economics , programming language , geography
Topic‐focused multidocument summarization has been a challenging task because the created summary is required to be biased to the given topic or query. Existing methods consider the given topic as a single coarse unit, and then directly incorporate the relevance between each sentence and the single topic into the sentence evaluation process. However, the given topic is usually not well defined and it consists of a few explicit or implicit subtopics. In this study, the related subtopics are discovered from the topic's narrative text or document set through topic analysis techniques. Then, the sentence relationships against each subtopic are considered as an individual modality and the multimodality manifold‐ranking method is proposed to evaluate and rank sentences by fusing the multiple modalities. Experimental results on the DUC benchmark data sets show the promising results of our proposed methods.