Context Aggregation with Topic-focused Summarization for Personalized Medical Dialogue Generation
Zhengyuan Liu, Siti Salleh, Pavitra Krishnaswamy, Nancy Chen
Abstract
In the realm of dialogue systems, generated responses often lack personalization. This is particularly true in the medical domain, where research is limited by scarce available domain-specific data and the complexities of modeling medical context and persona information. In this work, we investigate the potential of harnessing large language models for personalized medical dialogue generation. In particular, to better aggregate the long conversational context, we adopt topic-focused summarization to distill core information from the dialogue history, and use such information to guide the conversation flow and generated content. Drawing inspiration from real-world telehealth conversations, we outline a comprehensive pipeline encompassing data processing, profile construction, and domain adaptation. This work not only highlights our technical approach but also shares distilled insights from the data preparation and model construction phases.- Anthology ID:
- 2024.clinicalnlp-1.27
- Volume:
- Proceedings of the 6th Clinical Natural Language Processing Workshop
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
- Venues:
- ClinicalNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 310–321
- Language:
- URL:
- https://aclanthology.org/2024.clinicalnlp-1.27
- DOI:
- Cite (ACL):
- Zhengyuan Liu, Siti Salleh, Pavitra Krishnaswamy, and Nancy Chen. 2024. Context Aggregation with Topic-focused Summarization for Personalized Medical Dialogue Generation. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 310–321, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Context Aggregation with Topic-focused Summarization for Personalized Medical Dialogue Generation (Liu et al., ClinicalNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.clinicalnlp-1.27.pdf