While automatic summarization techniques have made significant advancements, their primary focus has been on summarizing short news articles or documents that have clear structural patterns like scientific articles or government reports. There has not been much exploration into developing efficient methods for summarizing financial documents, which often contain complex facts and figures. Here, we study the problem of bullet point summarization of long Earning Call Transcripts (ECTs) using the recently released ECTSum dataset. We leverage an unsupervised question-based extractive module followed by a parameter efficient instruction-tuned abstractive module to solve this task. Our proposed model FLAN-FinBPS achieves new state-of-the-art performances outperforming the strongest baseline with 14.88% average ROUGE score gain, and is capable of generating factually consistent bullet point summaries that capture the important facts discussed in the ECTs.

翻译：尽管自动摘要技术已取得显著进展，其核心应用仍集中于短新闻文章或具有清晰结构模式的文档（如科学论文或政府报告）。针对常包含复杂事实与数据的金融文档高效摘要方法，学界尚未展开充分探索。本文基于近期发布的ECTSum数据集，研究长财务电话会议记录（ECTs）的要点摘要问题。我们采用无监督的基于问题的提取模块，后接参数高效指令微调生成模块以完成此任务。所提出的FLAN-FinBPS模型实现了最新最优性能，平均ROUGE评分较最强基线提升14.88%，并能够生成事实一致的要点摘要，准确捕捉财务电话会议记录中讨论的关键事实信息。