Papers by Swaroop Nath
One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation (2024.acl-long)
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Tejpalsingh Siledar, Swaroop Nath, Sankara Muddu, Rupasai Rangaraju, Swaprava Nath, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera
| Challenge: | Existing evaluation methods for opinion summarizations lack adequate opinion summary evaluation datasets. |
| Approach: | They propose a dataset that combines 7 dimensions crucial to opinion summaries . they propose OP-I-PROMPT, a dimension-independent prompt, and OP PROMPTS, . |
| Outcome: | The proposed model achieves a Spearman correlation of 0.70 with human judgments, surpassing prior methods. |
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Query-focused Summarization (QfS) is a system that generates summaries from document(s) based on a query. |
| Approach: | They propose a Query-focused Summarization approach that uses a generalization of Reinforcement Learning (RL) for Natural Language Generation and a better semantic similarity reward. |
| Outcome: | The proposed approach improves on the ROUGE-L metric and in a benchmark dataset. |