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
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 193–197
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/I17-2033/
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/I17-2033.pdf