用多偏置框架提升情感解释摘要的准确性
Query-Focused Extractive Summarization for Sentiment Explanation
- 设计多偏置框架缓解查询与文本间的语言差异
- 在真实数据集上优于基线模型，提升情感解释效果
- 适合需要精准分析客户反馈的业务场景
客户反馈的建设性分析通常需要从大量文本中确定情绪成因。为提升此类工作的效率，我们采用查询聚焦摘要（QFS）任务。现有模型常因查询与源文档间语言不一致而受限。本文提出一种领域无关的通用多偏置框架，以弥合该差距，并针对情感解释问题，引入基于情感的偏置与查询扩展策略。实验在真实世界专有情感感知的QFS数据集上验证，结果优于基线模型。
原文摘要 · Abstract (English)
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.
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