{"ID":2870962,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11989","arxiv_id":"2509.11989","title":"Query-Focused Extractive Summarization for Sentiment Explanation","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.11989","url_pdf":"https://arxiv.org/pdf/2509.11989v1","authors":"[\"Ahmed Moubtahij\",\"Sylvie Ratté\",\"Yazid Attabi\",\"Maxime Dumas\"]","published":"2025-09-15T14:41:18Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\"]","methods":"[]","has_code":false}