2,409 research outputs found
Abstractive Multi-Document Summarization via Phrase Selection and Merging
We propose an abstraction-based multi-document summarization framework that
can construct new sentences by exploring more fine-grained syntactic units than
sentences, namely, noun/verb phrases. Different from existing abstraction-based
approaches, our method first constructs a pool of concepts and facts
represented by phrases from the input documents. Then new sentences are
generated by selecting and merging informative phrases to maximize the salience
of phrases and meanwhile satisfy the sentence construction constraints. We
employ integer linear optimization for conducting phrase selection and merging
simultaneously in order to achieve the global optimal solution for a summary.
Experimental results on the benchmark data set TAC 2011 show that our framework
outperforms the state-of-the-art models under automated pyramid evaluation
metric, and achieves reasonably well results on manual linguistic quality
evaluation.Comment: 11 pages, 1 figure, accepted as a full paper at ACL 201
Extractive multi document summarization using harmony search algorithm
The exponential growth of information on the internet makes it troublesome for users to get valuable information. Text summarization is the process to overcome such a problem. An adequate summary must have wide coverage, high diversity, and high readability. In this article, a new method for multi-document summarization has been supposed based on a harmony search algorithm that optimizes the coverage, diversity, and readability. Concerning the benchmark dataset Text Analysis Conference (TAC-2011), the ROUGE package used to measure the effectiveness of the proposed model. The calculated results support the effectiveness of the proposed approach
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