1 citations · 1 across the 5 of their papers we have counts for
8 papers
LLMs as Architects and Critics for Multi-Source Opinion Summarization
Anuj Attri, Arnav Attri, Pushpak Bhattacharyya +4
Multi-source Opinion Summarization (M-OS) extends beyond traditional opinion summarization by incorporating additional sources of product metadata such as descriptions, key feature…
"This Suits You the Best": Query Focused Comparative Explainable Summarization
Arnav Attri, Anuj Attri, Pushpak Bhattacharyya +4
Product recommendations inherently involve comparisons, yet traditional opinion summarization often fails to provide holistic comparative insights. We propose the novel task of gen…
Why We Feel What We Feel: Joint Detection of Emotions and Their Opinion Triggers in E-commerce
Arnav Attri, Anuj Attri, Pushpak Bhattacharyya +4
Customer reviews on e-commerce platforms capture critical affective signals that drive purchasing decisions. However, no existing research has explored the joint task of emotion de…
A Teacher Is Worth A Million Instructions
Nikhil Kothari, Ravindra Nayak, Shreyas Shetty +2
Large Language Models(LLMs) have shown exceptional abilities, yet training these models can be quite challenging. There is a strong dependence on the quality of data and finding th…
Distilling Opinions at Scale: Incremental Opinion Summarization using XL-OPSUMM
Sri Raghava Muddu, Rupasai Rangaraju, Tejpalsingh Siledar +8
Opinion summarization in e-commerce encapsulates the collective views of numerous users about a product based on their reviews. Typically, a product on an e-commerce platform has t…
Product Description and QA Assisted Self-Supervised Opinion Summarization
Tejpalsingh Siledar, Rupasai Rangaraju, Sankara Sri Raghava Ravindra Muddu +7
In e-commerce, opinion summarization is the process of summarizing the consensus opinions found in product reviews. However, the potential of additional sources such as product des…
Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization
Swaroop Nath, Tejpalsingh Siledar, Sankara Sri Raghava Ravindra Muddu +8
Reinforcement Learning from Human Feedback (RLHF) has become a dominating strategy in aligning Language Models (LMs) with human values/goals. The key to the strategy is learning a…
One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation
Tejpalsingh Siledar, Swaroop Nath, Sankara Sri Raghava Ravindra Muddu +8
Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human…