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Moch Zawaruddin Abdullah
Institut Teknologi Sepuluh Nopember
Computer Science & IT

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Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging for Multi-Document Summarization Agus Zainal Arifin; Moch Zawaruddin Abdullah; Ahmad Wahyu Rosyadi; Desepta Isna Ulumi; Aminul Wahib; Rizka Wakhidatus Sholikah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8431

Abstract

Automatic multi-document summarization needs to find representative sentences not only by sentence distribution to select the most important sentence but also by how informative a term is in a sentence. Sentence distribution is suitable for obtaining important sentences by determining frequent and well-spread words in the corpus but ignores the grammatical information that indicates instructive content. The presence or absence of informative content in a sentence can be indicated by grammatical information which is carried by part of speech (POS) labels. In this paper, we propose a new sentence weighting method by incorporating sentence distribution and POS tagging for multi-document summarization. Similarity-based Histogram Clustering (SHC) is used to cluster sentences in the data set. Cluster ordering is based on cluster importance to determine the important clusters. Sentence extraction based on sentence distribution and POS tagging is introduced to extract the representative sentences from the ordered clusters. The results of the experiment on the Document Understanding Conferences (DUC) 2004 are compared with those of the Sentence Distribution Method. Our proposed method achieved better results with an increasing rate of 5.41% on ROUGE-1 and 0.62% on ROUGE-2.
Co-Authors Agus Zainal Arifin Aminul Wahib Desepta Isna Ulumi Rizka Wakhidatus Sholikah Rosyadi, Ahmad Wahyu