Summary of Bridging Context Gaps: Enhancing Comprehension in Long-form Social Conversations Through Contextualized Excerpts, by Shrestha Mohanty et al.
Bridging Context Gaps: Enhancing Comprehension in Long-Form Social Conversations Through Contextualized Excerpts
by Shrestha Mohanty, Sarah Xuan, Jacob Jobraeel, Anurag Kumar, Deb Roy, Jad Kabbara
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates ways to enhance comprehension in small-group conversations, which are crucial for sharing personal stories and experiences on social issues. By sharing highlighted excerpts from one conversation in another setting, people can better understand relevant topics. However, this approach is limited when conversations lack context or key elements. To address this, the authors explore how Large Language Models (LLMs) can provide socially relevant context to improve comprehension, readability, and empathy. The study presents approaches for effective contextualization and shows significant improvements in understanding through subjective and objective evaluations. While LLMs are valuable, they struggle with capturing social aspects. The paper also releases the Human-annotated Salient Excerpts (HSE) dataset to support future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping people understand conversations better. It looks at ways to share parts of conversations that are important and relevant. This can help people learn from each other’s experiences and perspectives. The problem is that these shared parts might not have the right context or information, which makes it hard for others to understand. To solve this, the authors use special computer models called Large Language Models (LLMs) to add more context. They show that this helps people understand better, but there are still some limitations. The paper also gives a dataset with examples of important parts of conversations so other researchers can build on this work. |
