Query-Focused Extractive Summarization for Sentiment Explanation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912587741921280 |
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| author | Moubtahij, Ahmed Ratté, Sylvie Attabi, Yazid Dumas, Maxime |
| author_facet | Moubtahij, Ahmed Ratté, Sylvie Attabi, Yazid Dumas, Maxime |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Query-Focused Extractive Summarization for Sentiment Explanation Moubtahij, Ahmed Ratté, Sylvie Attabi, Yazid Dumas, Maxime Computation and Language Machine Learning 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. |
| title | Query-Focused Extractive Summarization for Sentiment Explanation |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.11989 |