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Published in:
2017 | OriginalPaper | Chapter
Deep Learning in the Domain of Multi-Document Text Summarization
Authors : Rajendra Kumar Roul, Jajati Keshari Sahoo, Rohan Goel
Published in: Pattern Recognition and Machine Intelligence
Publisher: Springer International Publishing
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Abstract
Text summarization is the process of generating a shorter version of the input text which captures its most important information. This paper addresses and tries to solve the problem of extractive text summarization which works by selecting a subset of phrases or sentences from the original document(s) to form a summary. Selections of such sentences are done based on certain criteria which formulates a feature set. Multilayer ELM (Extreme Learning Machine) which is based on the underlying deep network architecture is trained over this feature set to classify the sentences as important or unimportant. The used approach is unique and highlights the effectiveness of Multilayer ELM and its stability for usage in the domain of text summarization. Effectiveness of Multilayer ELM is justified by the experimental results on DUC and TAC datasets wherein it significantly outperforms the other well known classifiers.