Automatic Text Summarization Using Reinforcement Learning with Embedding Features

Gyoung Ho Lee, Kong Joo Lee


Abstract
An automatic text summarization system can automatically generate a short and brief summary that contains a main concept of an original document. In this work, we explore the advantages of simple embedding features in Reinforcement leaning approach to automatic text summarization tasks. In addition, we propose a novel deep learning network for estimating Q-values used in Reinforcement learning. We evaluate our model by using ROUGE scores with DUC 2001, 2002, Wikipedia, ACL-ARC data. Evaluation results show that our model is competitive with the previous models.
Anthology ID:
I17-2033
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
193–197
Language:
URL:
https://aclanthology.org/I17-2033
DOI:
Bibkey:
Cite (ACL):
Gyoung Ho Lee and Kong Joo Lee. 2017. Automatic Text Summarization Using Reinforcement Learning with Embedding Features. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 193–197, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Automatic Text Summarization Using Reinforcement Learning with Embedding Features (Lee & Lee, IJCNLP 2017)
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PDF:
https://aclanthology.org/I17-2033.pdf