An Approach for Multi-Document Text Summarization Using Extreme Learning Machine and LexRank


  • Wedad Abdul Khuder Naser Mustansiriyah University Iraq.



Summarization, Duc-2002, Lexrank, Extreme Learning Machine


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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How to Cite

Wedad Abdul Khuder Naser. (2021). An Approach for Multi-Document Text Summarization Using Extreme Learning Machine and LexRank. International Journal of Engineering Research and Advanced Technology (ijerat), 7(5), 19-28.