A Multi-Task Learning Approach for Summarization of Dialogues
Saprativa Bhattacharjee, Kartik Shinde, Tirthankar Ghosal, Asif Ekbal
Abstract
We describe our multi-task learning based ap- proach for summarization of real-life dialogues as part of the DialogSum Challenge shared task at INLG 2022. Our approach intends to im- prove the main task of abstractive summariza- tion of dialogues through the auxiliary tasks of extractive summarization, novelty detection and language modeling. We conduct extensive experimentation with different combinations of tasks and compare the results. In addition, we also incorporate the topic information provided with the dataset to perform topic-aware sum- marization. We report the results of automatic evaluation of the generated summaries in terms of ROUGE and BERTScore.- Anthology ID:
- 2022.inlg-genchal.16
- Volume:
- Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges
- Month:
- July
- Year:
- 2022
- Address:
- Waterville, Maine, USA and virtual meeting
- Editors:
- Samira Shaikh, Thiago Ferreira, Amanda Stent
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 110–120
- Language:
- URL:
- https://aclanthology.org/2022.inlg-genchal.16
- DOI:
- Cite (ACL):
- Saprativa Bhattacharjee, Kartik Shinde, Tirthankar Ghosal, and Asif Ekbal. 2022. A Multi-Task Learning Approach for Summarization of Dialogues. In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges, pages 110–120, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- A Multi-Task Learning Approach for Summarization of Dialogues (Bhattacharjee et al., INLG 2022)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2022.inlg-genchal.16.pdf