GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations

• URL: http://arxiv.org/abs/2104.08578v1
• Date: Sat, 17 Apr 2021 15:42:01 GMT
• Title: GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations
• Authors: Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle Lee, Anish Acharya, Rajiv Ratn Shah
• Abstract summary: We introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset. GupShup contains over 6,831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English. We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation.
• Score: 28.693328393260906
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Code-switching is the communication phenomenon where speakers switch between different languages during a conversation. With the widespread adoption of conversational agents and chat platforms, code-switching has become an integral part of written conversations in many multi-lingual communities worldwide. This makes it essential to develop techniques for summarizing and understanding these conversations. Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6,831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English. We present a detailed account of the entire data collection and annotation processes. We analyze the dataset using various code-switching statistics. We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation. Our results show that multi-lingual mBART and multi-view seq2seq models obtain the best performances on the new dataset

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