Presentation information

Interactive Session

[3Rin4] Interactive 1

Thu. Jun 11, 2020 1:40 PM - 3:20 PM Room R01 (jsai2020online-2-33)

[3Rin4-81] Length-controllable Abstractive Summarization by Guiding with Summary Prototype

〇Itsumi Saito1, Kyosuke Nishida1, Kosuke Nishida1, Atsushi Otsuka1, Hisako Asano1, Junji Tomita1, Hiroyuki Shindo2, Yuji Matsumoto2,3 (1.NTT Media Intelligence Laboratories, 2.Nara Institute of Science and Technology, 3.RIKEN Center for Advanced Intelligence Project)

Keywords:Abstractive Summarization, Length Control, Extraction of Important Words

We propose a new length-controllable abstractive summarization model. Recent state-of-the-art abstractive summarization models based on encoder-decoder models generate only one summary per source text. However, controllable summarization, especially of the length, is an important aspect for practical applications. Previous studies on length-controllable abstractive summarization incorporate length embeddings in the decoder module for controlling the summary length. Unlike these models, our length-controllable abstractive summarization model incorporates a word-level extractive module that determines important parts of the source text in the encoder-decoder model instead of length embeddings. This module determines important parts of the source text that should be included as a summary within a length constraint. Since the extractive module becomes a guide to both the content and length of the summary, our model can generate an informative and length-controlled summary. Experiments with the CNN/Daily Mail dataset and the NEWSROOM dataset show that our model outperformed previous models in length-controlled settings.

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