GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations
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
Keywords
Cite
@article{arxiv.2104.08578,
title = {GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations},
author = {Laiba Mehnaz and Debanjan Mahata and Rakesh Gosangi and Uma Sushmitha Gunturi and Riya Jain and Gauri Gupta and Amardeep Kumar and Isabelle Lee and Anish Acharya and Rajiv Ratn Shah},
journal= {arXiv preprint arXiv:2104.08578},
year = {2021}
}