Abstractive Thai Opinion Summarization

Author:

Chaowalit Orawan1,Sornil Ohm2

Affiliation:

1. National Institute of Development Administration

2. National Institute of Development

Abstract

With the advancement in the Internet technology, customers can easily share opinions on services and products in forms of reviews. There can be large amounts of reviews for popular products. Manually summarizing those reviews for important issues is a daunting task. Automatic opinion summarization is a solution to the problem. The task is more complicated for reviews written in Thai language. Thai words are written continuously without space, there is no symbol to signify the end of a sentence, and many reviews are written informally, thus accurate word identification and linguistic annotation cannot be relied upon. This research proposes a novel technique to generate abstractive summaries of customer reviews written in Thai language. The proposed technique, which consists of the local and the global models, is evaluated using actual reviews of fifty randomly selected products from a popular cosmetic website. The results show that the local model outperforms the other model and the two baseline methods both quantitatively and qualitatively.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference12 articles.

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2. K. Ganesan, C. Zhai, and J. Han: Opinogsis A Graph-based Approach to Abstractive Summarization of Highly Redundant Opinions, in Proceedings of the 23rd International Conference on Computational Linguistics, Stroudsburg, PA, USA (2010), p.340–348.

3. K. Filippova: Multi-sentence Compression Finding Shortest Paths in Word Graphs, in Proceedings of the 23rd International Conference on Computational Linguistics, Stroudsburg, PA, USA (2010), p.322–330.

4. K. Ganesan, C. Zhai, and E. Viegas: Micropinion Generation An Unsupervised Approach to Generating Ultra-concise Summaries of Opinions, in Proceedings of the 21st International Conference on World Wide Web, New York, NY, USA (2012), p.869–878.

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