Query-Focused Extractive Summarization for Sentiment Explanation
This work addresses the need for efficient sentiment analysis from client feedback to improve productivity, though it appears incremental with specialized adaptations.
The paper tackled the problem of bridging the linguistic gap between queries and source documents in query-focused summarization for sentiment explanation, achieving results that outperform baseline models on a real-world proprietary dataset.
Constructive analysis of feedback from clients often requires determining the cause of their sentiment from a substantial amount of text documents. 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 documents. We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level; we then 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.