用大模型补全长对话片段缺失的社会背景，提升理解与共情。
Bridging Context Gaps: Enhancing Comprehension in Long-Form Social Conversations Through Contextualized Excerpts
- 用大模型自动补充对话片段的社交上下文，还原关键背景。
- 主观评估显示理解度显著提升，客观指标也验证有效性。
- 适合研究社会对话理解、情感共情或对话摘要的学者使用。
我们关注小规模录音对话中的理解增强问题，这类对话常用于分享个人经历与社会议题。通过在后续对话中共享精选片段，可促进跨群体对关键议题的理解与共情。但直接截取片段会丢失原对话中的关键背景，尤其在内容丰富、主题复杂的长对话中更为严重。为此，我们探索如何利用大语言模型（LLMs）为这些片段注入相关社会背景。提出有效上下文补充方法，显著提升理解力、可读性与共情度。实验表明，在主观与客观评估中均有明显改善。尽管如此，当前模型仍难以准确捕捉核心社会要素。为此，我们发布了人类标注的突出片段数据集（HSE），以支持未来研究。此外，经上下文增强的片段还能生成更聚焦、全面的对话摘要。
原文摘要 · Abstract (English)
We focus on enhancing comprehension in small-group recorded conversations, which serve as a medium to bring people together and provide a space for sharing personal stories and experiences on crucial social matters. One way to parse and convey information from these conversations is by sharing highlighted excerpts in subsequent conversations. This can help promote a collective understanding of relevant issues, by highlighting perspectives and experiences to other groups of people who might otherwise be unfamiliar with and thus unable to relate to these experiences. The primary challenge that arises then is that excerpts taken from one conversation and shared in another setting might be missing crucial context or key elements that were previously introduced in the original conversation. This problem is exacerbated when conversations become lengthier and richer in themes and shared experiences. To address this, we explore how Large Language Models (LLMs) can enrich these excerpts by providing socially relevant context. We present approaches for effective contextualization to improve comprehension, readability, and empathy. We show significant improvements in understanding, as assessed through subjective and objective evaluations. While LLMs can offer valuable context, they struggle with capturing key social aspects. We release the Human-annotated Salient Excerpts (HSE) dataset to support future work. Additionally, we show how context-enriched excerpts can provide more focused and comprehensive conversation summaries.
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