让摘要更懂情绪，提升社交文本提炼效果
Sentiment-Aware Extractive and Abstractive Summarization for Unstructured Text Mining
- 融合情感信号到抽取与生成过程，增强情绪捕捉能力
- 在真实用户评论上提升主题相关性与情感准确性
- 适合品牌监控、舆情分析等需要情绪感知的场景
随着社交媒体、评论和论坛中非结构化数据的快速增长，信息系统的文本挖掘变得至关重要，以提取可操作的洞察。摘要能将碎片化、情绪丰富的帖子浓缩，但现有方法（针对结构化新闻优化）在处理嘈杂、非正式内容时表现不佳。情感线索对品牌监测和市场分析等信息系统任务至关重要，但很少有研究将情感建模融入短文本摘要。我们提出一种情感感知框架，通过在排序和生成过程中嵌入情感信号，扩展了抽取式（TextRank）和抽象式（UniLM）方法。该双架构设计提升了情感细微差别的捕捉与主题相关性，生成简洁且富含情感的摘要，有助于在动态网络环境中实现及时干预与战略决策。
原文摘要 · Abstract (English)
With the rapid growth of unstructured data from social media, reviews, and forums, text mining has become essential in Information Systems (IS) for extracting actionable insights. Summarization can condense fragmented, emotion-rich posts, but existing methods-optimized for structured news-struggle with noisy, informal content. Emotional cues are critical for IS tasks such as brand monitoring and market analysis, yet few studies integrate sentiment modeling into summarization of short user-generated texts. We propose a sentiment-aware framework extending extractive (TextRank) and abstractive (UniLM) approaches by embedding sentiment signals into ranking and generation processes. This dual design improves the capture of emotional nuances and thematic relevance, producing concise, sentiment-enriched summaries that enhance timely interventions and strategic decision-making in dynamic online environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。