Toward abstractive multi-document summarization using submodular function-based framework, sentence compression and merging

Automatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary - namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure.

Authors

• Tanvee, Moin Mahmud
• University of Lethbridge. Faculty of Arts and Science

• automatic text summarization
• abstraction-based
• submodular function
• generic-focused summarization
• query-focused summarization
• greedy algorithm
• Natural language processing (Computer science) -- Research
• Querying (Computer science)
• Database searching
• Parsing (Computer grammar)
• Information retrieval
• Question-answering systems -- Research
• Computer science -- Mathematics

Identifier

hdl:10133/4841

OAI identifier oai:identifier

oai:opus.uleth.ca:10133/4841