Summary of Instruction-guided Bullet Point Summarization Of Long Financial Earnings Call Transcripts, by Subhendu Khatuya et al.
Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts
by Subhendu Khatuya, Koushiki Sinha, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Information Retrieval (cs.IR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores automatic summarization techniques for financial documents, specifically Earning Call Transcripts (ECTs), which are often lengthy and contain complex data. The authors propose a hybrid approach combining unsupervised question-based extractive and instruction-tuned abstractive modules to generate factually consistent bullet point summaries. The model, FLAN-FinBPS, achieves state-of-the-art performance on the ECTSum dataset with an average ROUGE score gain of 14.88% compared to the strongest baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to summarize long financial documents called Earning Call Transcripts. These documents are important for people who invest in companies and need to understand what’s going on. The authors want to make it easier to get the most important information out of these documents by creating a new way to do automatic summarization. They use a combination of two different methods to summarize the documents, and their approach works really well. |
Keywords
» Artificial intelligence » Rouge » Summarization » Unsupervised
