Papers by Manjunath Hegde

3 papers
Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling (2024.naacl-long)

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Challenge: Existing methods to automatically annotate relevant numerals (GAAP metrics) occurring in financial documents are not cost-effective nor scalable.
Approach: They propose a generative paradigm for annotating GAAP metrics with XBRL tags using metric metadata and a parameter efficient model using LoRA.
Outcome: The proposed model outperforms baseline models on two financial numeric labeling datasets and outperformed several strong baseline models.
Financial Numeric Extreme Labelling: A dataset and benchmarking (2023.findings-acl)

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Challenge: In 2019, the SEC mandates that all public companies file periodic financial statements that should contain numerals annotated with a particular label.
Approach: They propose to use a dataset to automate the assignment of a label to a particular numeral span in a sentence from an extremely large label set.
Outcome: The proposed solution outperforms the previous approaches but is less frequent than the pipeline solution.
ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts (2022.emnlp-main)

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Challenge: ECTSum is a dataset for bullet-point summarization of earnings calls hosted by publicly traded companies.
Approach: They propose a dataset with transcripts of earnings calls and bullet point summaries derived from Reuters articles.
Outcome: The proposed dataset compares transcripts of earnings calls hosted by publicly traded companies with experts-written bullet point summaries derived from Reuters articles .

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