Text Segregation On Asynchronous Group Chat
Abhishek Kumar Sinha, Midhush Manohar T.K., Srikumar Subramanian, Bhaskarjyoti Das · Procedia Computer Science · 2020
Ability to successfully segregate texts around different topics can lead to successful text summarization. Though text summarization has been well researched, summarization of multi-party asynchronous chat has not been attempted. Popular systems such as Whatsapp, Telegram etc. have such kind of chat scenarios. Such chats are asynchronous as participants can respond to a thread after a long delay and not as an immediate reply to ongoing conversation. Though there are few chat data sets available, annotated data sets of such multi-party asynchronous chats are not yet available even though summarization in this particular domain presents a good use case for chat participants. With such a summarization feature, a user can quickly review the summary of past conversations during his period of inactivity. However, this is challenging as any summarization attempt on a data set like this must address three aspects i.e. threads of discussion, time window and topic/sub-topics in an inter-woven sequence of messages that does not have any usual sense of sequence. In the absence of annotated data sets and challenge of addressing these three aspects in seemingly non-sequential messages, machine learning based approaches will not work well. In this work, an innovative pipeline based on heuristics is designed to address text segregation for such a scenario. Once text segregation is achieved, text summarization may become a lesser challenge. It is observed that this approach is promising enough to warrant further research and enhancements.