Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts (2405.06669v1)

Published 3 May 2024 in cs.CL, cs.CE, cs.IR, and cs.LG

Abstract: 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. Nouf Ibrahim Altmami and Mohamed El Bachir Menai. 2022. Automatic summarization of scientific articles: A survey. Journal of King Saud University-Computer and Information Sciences 34, 4 (2022), 1011–1028.
  2. Aspect-controllable opinion summarization. arXiv preprint arXiv:2109.03171 (2021).
  3. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150 (2020).
  4. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 (2022).
  5. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. https://doi.org/10.18653/v1/N19-1423
  6. The financial narrative summarisation shared task (FNS 2020). In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation. 1–12.
  7. The Financial Narrative Summarisation Shared Task (FNS 2022). In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022. European Language Resources Association, Marseille, France, 43–52. https://aclanthology.org/2022.fnp-1.6
  8. Günes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research 22 (2004), 457–479.
  9. Company-Oriented Extractive Summarization of Financial News. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009). Association for Computational Linguistics, Athens, Greece, 246–254. https://aclanthology.org/E09-1029
  10. LongT5: Efficient text-to-text transformer for long sequences. arXiv preprint arXiv:2112.07916 (2021).
  11. Document summarization based on data reconstruction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 26. 620–626.
  12. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021).
  13. Extractive summarization using multi-task learning with document classification. In Proceedings of the 2017 Conference on empirical methods in natural language processing. 2101–2110.
  14. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications 78 (2019), 15169–15211.
  15. SummaC: Re-visiting NLI-based models for inconsistency detection in summarization. Transactions of the Association for Computational Linguistics 10 (2022), 163–177.
  16. Jochen L Leidner. 2020. Summarization in the financial and regulatory domain. In Trends and Applications of Text Summarization Techniques. IGI Global, 187–215.
  17. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019).
  18. Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74–81.
  19. The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. arXiv:2301.13688 [cs.AI]
  20. ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 10893–10906. https://doi.org/10.18653/v1/2022.emnlp-main.748
  21. Read what you need: Controllable aspect-based opinion summarization of tourist reviews. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 1825–1828.
  22. Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31.
  23. Extractive Summarization of Financial Earnings Call Transcripts: Or: When GREP Beat BERT. In European Conference on Information Retrieval. Springer, 3–15.
  24. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21, 1 (2020), 5485–5551.
  25. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv:1910.10683 [cs.LG]
  26. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019).
  27. Automatic text summarization for government news reports based on multiple features. The Journal of Supercomputing (2023), 1–17.
  28. Big bird: Transformers for longer sequences. Advances in neural information processing systems 33 (2020), 17283–17297.
  29. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In International Conference on Machine Learning. PMLR, 11328–11339.
  30. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019).
  31. Hao Zheng and Mirella Lapata. 2019. Sentence centrality revisited for unsupervised summarization. arXiv preprint arXiv:1906.03508 (2019).
  32. Extractive summarization as text matching. arXiv preprint arXiv:2004.08795 (2020).
  33. The financial narrative summarisation shared task FNS 2021. In Proceedings of the 3rd Financial Narrative Processing Workshop. 120–125.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now