Query-focused extractive summarization for sentiment explanation

Abstract

Constructive analysis of clients’ feedback often requires determining the cause of their sentiment from a substantial amount of text documents. In order to assist and improve the productivity of such endeavors, we leverage the task of Query-Focused Summarization (QFS). Models of this task are often impeded by the linguistic dissonance between the query and the source document(s). We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level, then we formulate specialized approaches for the problem of sentiment explanation through sentiment-based biases and query expansion. We achieve experimental results outperforming baseline models on a real-world proprietary sentiment-aware QFS dataset.

Date	26 Apr 2023
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Sylvie Ratté (Supervisor) & Yazid Attabi (Co-supervisor)