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Extractive multi-document text summarization using dolphin swarm optimization approach
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DOI：10.1007/s11042-020-10176-1.png)
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Abstract
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Nowadays, extracting the desired information from internet source is a challenging task because of a large amount of information available on the internet. So, we propose a new extractive based approach for multi-document text summarization to extract useful information from multi-document. Initially, the redundant contents in the document create a single text file from the multiple text file document. The content coverage and non-redundancy features are achieved by Word Mover Distance (WMD) and Modified Normalized Google Distance (M-NGD) (WM) Hybrid Weight Method based similarity approaches. For feature weight optimization, we use the Dolphin swarm optimization (DSO) which is a metaheuristic approach. The proposed approach is tested under python with multiling 2013 dataset and the performances have been evaluated with ROUGE and AutoSummENG metrics. The investigational outcomes show that the proposed technique works well and very much effective for multi-document text summarization.
Keywords:
Dolphin swarm optimization
Multi-document summarization
Modified normalized Google distance
Word mover distance
Similarity
Non-redundancy
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