Long-Span Summarization via Local Attention and Content Selection

Potsawee Manakul, Mark Gales


Abstract
Transformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization. Typically these systems are trained by fine-tuning a large pre-trained model to the target task. One issue with these transformer-based models is that they do not scale well in terms of memory and compute requirements as the input length grows. Thus, for long document summarization, it can be challenging to train or fine-tune these models. In this work, we exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. These approaches are compared on a range of network configurations. Experiments are carried out on standard long-span summarization tasks, including Spotify Podcast, arXiv, and PubMed datasets. We demonstrate that by combining these methods, we can achieve state-of-the-art results on all three tasks in the ROUGE scores. Moreover, without a large-scale GPU card, our approach can achieve comparable or better results than existing approaches.
Anthology ID:
2021.acl-long.470
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6026–6041
Language:
URL:
https://aclanthology.org/2021.acl-long.470
DOI:
10.18653/v1/2021.acl-long.470
Bibkey:
Cite (ACL):
Potsawee Manakul and Mark Gales. 2021. Long-Span Summarization via Local Attention and Content Selection. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6026–6041, Online. Association for Computational Linguistics.
Cite (Informal):
Long-Span Summarization via Local Attention and Content Selection (Manakul & Gales, ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.470.pdf
Code
 potsawee/longsum0
Data
CNN/Daily Mail