Interactive Query-Assisted Summarization via Deep Reinforcement Learning

Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Ido Dagan, Yael Amsterdamer


Abstract
Interactive summarization is a task that facilitates user-guided exploration of information within a document set. While one would like to employ state of the art neural models to improve the quality of interactive summarization, many such technologies cannot ingest the full document set or cannot operate at sufficient speed for interactivity. To that end, we propose two novel deep reinforcement learning models for the task that address, respectively, the subtask of summarizing salient information that adheres to user queries, and the subtask of listing suggested queries to assist users throughout their exploration. In particular, our models allow encoding the interactive session state and history to refrain from redundancy. Together, these models compose a state of the art solution that addresses all of the task requirements. We compare our solution to a recent interactive summarization system, and show through an experimental study involving real users that our models are able to improve informativeness while preserving positive user experience.
Anthology ID:
2022.naacl-main.184
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2551–2568
Language:
URL:
https://aclanthology.org/2022.naacl-main.184
DOI:
10.18653/v1/2022.naacl-main.184
Bibkey:
Cite (ACL):
Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Ido Dagan, and Yael Amsterdamer. 2022. Interactive Query-Assisted Summarization via Deep Reinforcement Learning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2551–2568, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Interactive Query-Assisted Summarization via Deep Reinforcement Learning (Shapira et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.184.pdf
Code
 orishapira/interexp_deeprl