  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
