2016 IEEE International Conference on Services Computing (SCC) (2016)
San Francisco, CA, USA
June 27, 2016 to July 2, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SCC.2016.13
It can be time consuming to search Internet news, due to multiple sources reporting repetitive information. Given a query and a set of relevant text articles, query-focused multi-document summarization (QMDS) aims to generate a fluent, well-organized, and compact summary that answers the query. While QMDS helps to summarize search results, most top-performing systems for this purpose remain largely extractive. Extractive summarization extracts a group of sentences and concatenates them. In this paper, we propose a summarization service based on abstractive QMDS using multi-sentence compression (MSC). Our proposed service generates a novel summary representing the gist of the content of the source document(s). Experiments using popular summarization benchmark datasets demonstrate the effectiveness of the proposed service.
Semantics, Internet, Australia, Clustering algorithms, Mathematical model, Greedy algorithms, Pragmatics
E. ShafieiBavani, M. Ebrahimi, R. Wong and F. Chen, "A Query-Based Summarization Service from Multiple News Sources," 2016 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA, 2016, pp. 42-49.