An Approach for Multi-Document Text Summarization Using Extreme Learning Machine and LexRank
DOI:
https://doi.org/10.31695/IJERAT.2021.3704Keywords:
Summarization, Duc-2002, Lexrank, Extreme Learning MachineAbstract
Due to the exponential growth of online textual data and the variety of its sources, there is a need to produce an accurate text summary with the least time and effort. Extractive multi-document text summarization methods are intended to automatically generate summaries from a document collection, covering the main content and avoiding redundant information. In this study, a new method for extractive multi-document summarization has been proposed based on the combination of supervised and unsupervised learning. Throughout the supervised learning, a set of seven features was extracted from each sentence in the document collection and introduces to the Extreme Learning Machine (ELM), to distinguish between important and unimportant sentences. A graph of important sentences was generated and assigned scores by the LexRank algorithm during the unsupervised learning. The performance of the proposed method on the DUC-2002 dataset was calculated using ROUGE evaluation metrics. The proposed method achieved a 0.47472 ROUGE for 200-word summaries and 0.54641 ROUGE for 400-word summaries.
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Copyright (c) 2021 Wedad Abdul Khuder Naser
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