Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914791472234496 |
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| author | Khatuya, Subhendu Sinha, Koushiki Ganguly, Niloy Ghosh, Saptarshi Goyal, Pawan |
| author_facet | Khatuya, Subhendu Sinha, Koushiki Ganguly, Niloy Ghosh, Saptarshi Goyal, Pawan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06669 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts Khatuya, Subhendu Sinha, Koushiki Ganguly, Niloy Ghosh, Saptarshi Goyal, Pawan Computation and Language Computational Engineering, Finance, and Science Information Retrieval Machine Learning 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. |
| title | Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts |
| topic | Computation and Language Computational Engineering, Finance, and Science Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2405.06669 |