The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries

Tatiana Vodolazova, Elena Lloret


Abstract
In this paper we describe how an abstractive text summarization method improved the informativeness of automatic summaries by integrating syntactic text simplification, subject-verb-object concept frequency scoring and a set of rules that transform text into its semantic representation. We analyzed the impact of each component of our approach on the quality of generated summaries and tested it on DUC 2002 dataset. Our experiments showed that our approach outperformed other state-of-the-art abstractive methods while maintaining acceptable linguistic quality and redundancy rate.
Anthology ID:
R19-1146
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1275–1284
Language:
URL:
https://aclanthology.org/R19-1146
DOI:
10.26615/978-954-452-056-4_146
Bibkey:
Cite (ACL):
Tatiana Vodolazova and Elena Lloret. 2019. The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1275–1284, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries (Vodolazova & Lloret, RANLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/R19-1146.pdf