Collabot: Personalized Group Chat Summarization

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining Pub Date : 2018-02-02 DOI:10.1145/3159652.3160588

N. Tepper, Anat Hashavit, Maya Barnea, Inbal Ronen, L. Leiba

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引用次数: 13

Abstract

In recent years, enterprise group chat collaboration tools, such as Slack, IBM»s Watson Workspace and Microsoft Teams, have presented unprecedented growth. With all the potential benefits of these tools - productivity increase and improved group communication - come significant challenges. Specifically, the 'always on' feature that makes it hard for users to cope with the load of conversational content and get up to speed after logging off for a while. In this demo, we present Collabot - a chat assistant service that implicitly learns users interests and social ties within a chat group and provides a personalized digest of missed content. Collabot assists users in coping with chat information overload by helping them understand the main topics discussed, collaborators, links and resources. This demo has two main contributions. First, we present a novel personalized group chat summarization algorithm; second the demonstration depicts a working implementation applied on different chat groups from different domains within IBM. A video, describing the demo can be found at https://www.youtube.com/watch?v=6cVsstiJ9vk.

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Collabot:个性化群组聊天汇总

近年来，企业群组聊天协作工具(如Slack、IBM的Watson Workspace和Microsoft Teams)呈现出前所未有的增长。随着这些工具的所有潜在好处——生产力的提高和群体沟通的改善——随之而来的是重大的挑战。具体来说，“永远在线”的功能会让用户在注销一段时间后很难处理会话内容的负载，也很难跟上速度。在这个演示中，我们展示了Collabot——一个聊天助手服务，它可以隐式地了解聊天组内用户的兴趣和社会关系，并提供个性化的遗漏内容摘要。Collabot通过帮助用户了解讨论的主要主题、合作者、链接和资源，帮助他们处理聊天信息过载。这个演示有两个主要贡献。首先，提出了一种新颖的个性化群聊汇总算法;其次，演示描述了应用于IBM内部不同域的不同聊天组的工作实现。描述演示的视频可以在https://www.youtube.com/watch?v=6cVsstiJ9vk上找到。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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