Abstract
In this work, we aim at developing an unsupervised abstractive summarization system in the multi-document setting. We design a paraphrastic sentence fusion model which jointly performs sentence fusion and paraphrasing using skip-gram word embedding model at the sentence level. Our model improves the information coverage and at the same time abstractiveness of the generated sentences. We conduct our experiments on the human-generated multi-sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Furthermore, we apply our sentence level model to implement an abstractive multi-document summarization system where documents usually contain a related set of sentences. We also propose an optimal solution for the classical summary length limit problem which was not addressed in the past research. For the document level summary, we conduct experiments on the datasets of two different domains (e.g., news article and user reviews) which are well suited for multi-document abstractive summarization. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods.- Anthology ID:
- C18-1102
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1191–1204
- Language:
- URL:
- https://aclanthology.org/C18-1102
- DOI:
- Cite (ACL):
- Mir Tafseer Nayeem, Tanvir Ahmed Fuad, and Yllias Chali. 2018. Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1191–1204, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion (Nayeem et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C18-1102.pdf