ECTSum：一个新的基准数据集的要点总结长期盈利呼叫成绩单
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts
摘要：
尽管在自动摘要方面取得了巨大的进步，但最先进的方法主要是在总结简短的新闻通讯文章或具有强烈布局偏见的文档（如科学文章或政府报告）方面表现出色。总结财务文件（包括事实和数字）的有效技术在很大程度上尚未探索，主要是由于缺乏合适的数据集。在这项工作中，我们提出了ECTSum，一个新的数据集，由上市公司托管的盈利电话会议（ECTs）的成绩单，作为文件，以及来自相应的路透社文章的简短专家撰写的电报式要点摘要。ECT是长的非结构化文档，没有任何规定的长度限制或格式。我们使用最先进的摘要器在各种指标上对数据集进行基准测试，以评估生成的摘要的内容质量和事实一致性。最后，我们提出了一种简单而有效的方法，ECT-BPS，以生成一组要点，精确地捕捉在电话中讨论的重要事实。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
