Abstract
In Natural Language Generation systems, personalization strategies - i.e, the use of information about a target author to generate text that (more) closely resembles human-produced language - have long been applied to improve results. The present work addresses one such strategy - namely, the use of Big Five personality information about the target author - applied to the case of abstractive text summarization using neural sequence-to-sequence models. Initial results suggest that having access to personality information does lead to more accurate (or human-like) text summaries, and paves the way for more robust systems of this kind.- Anthology ID:
- R19-1024
- 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:
- 205–212
- Language:
- URL:
- https://aclanthology.org/R19-1024
- DOI:
- 10.26615/978-954-452-056-4_024
- Cite (ACL):
- Pablo Costa and Ivandré Paraboni. 2019. Personality-dependent Neural Text Summarization. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 205–212, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Personality-dependent Neural Text Summarization (Costa & Paraboni, RANLP 2019)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/R19-1024.pdf