ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts

• URL: http://arxiv.org/abs/2210.12467v2
• Date: Wed, 26 Oct 2022 16:21:37 GMT
• Title: ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts
• Authors: Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
• Abstract summary: We present a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents. We also present a simple-yet-effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls.
• Score: 19.974530405492885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, including facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and short experts-written telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarizers across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple-yet-effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls.

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