Two-encoder pointer-generator network for summarizing segments of long articles

Junhao Li, Mizuho Iwaihara*


研究成果: Conference contribution


Usually long documents contain many sections and segments. In Wikipedia, one article can usually be divided into sections and one section can be divided into segments. But although one article is already divided into smaller segments, one segment can still be too long to read. So, we consider that segments should have a short summary for readers to grasp a quick view of the segment. This paper discusses applying neural summarization models including Seq2Seq model and pointer generator network model to segment summarization. These models for summarization can take target segments as the only input to the model. However, in our case, it is very likely that the remaining segments in the same article contain descriptions related to the target segment. Therefore, we propose several ways to extract an additional sequence from the whole article and then combine with the target segment, to be supplied as the input for summarization. We compare the results against the original models without additional sequences. Furthermore, we propose a new model that uses two encoders to process the target segment and additional sequence separately. Our results show our two-encoder model outperforms the original models in terms of ROGUE and METEOR scores.

ホスト出版物のタイトルWeb and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings
編集者Jie Shao, Man Lung Yiu, Masashi Toyoda, Dongxiang Zhang, Wei Wang, Bin Cui
出版社Springer Verlag
出版ステータスPublished - 2019
イベント3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 - Chengdu, China
継続期間: 2019 8月 12019 8月 3


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11641 LNCS


Conference3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般


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