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Rajdeep Mukherjee

5 accepted papers

2024

Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling

Subhendu Khatuya, Rajdeep Mukherjee, Akash Ghosh, Manjunath Hegde, Koustuv Dasgupta, Niloy Ganguly +2
NAACL 2024long

We study the problem of automatically annotating relevant numerals (GAAP metrics) occurring in the financial documents with their corresponding XBRL tags. Different from prior works, we investigate the feasibility of solving this extreme classification problem using a generative paradigm through ins…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2023

CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction

Rajdeep Mukherjee, Nithish Kannen, Saurabh Kumar Pandey, Pawan Goyal
EMNLP 2023long findings

Existing works on Aspect Sentiment Triplet Extraction (ASTE) explicitly focus on developing more efficient fine-tuning techniques for the task. Instead, our motivation is to come up with a generic approach that can improve the downstream performances of multiple ABSA tasks simultaneously. Towards th…

Cited by 0Source ↗code ↗Scholar ↗
2023

MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments

Debtanu Datta, Shubham Soni, Rajdeep Mukherjee, Saptarshi Ghosh
EMNLP 2023short main

Automatic summarization of legal case judgments is a practically important problem that has attracted substantial research efforts in many countries. In the context of the Indian judiciary, there is an additional complexity -- Indian legal case judgments are mostly written in complex English, but a…

Cited by 0Source ↗code ↗Scholar ↗
2022

ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts

Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh +5
EMNLP 2022main

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents,…

Cited by 48Source ↗PDF ↗code ↗Scholar ↗
2021

PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

Rajdeep Mukherjee, Tapas Nayak, Yash Butala, Sourangshu Bhattacharya, Pawan Goyal
EMNLP 2021main

Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment. Existing research efforts are majorly tagging-based. Among th…

Cited by 71Source ↗PDF ↗code ↗Scholar ↗