Papers by Sandeep Atluri
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization (2023.findings-eacl)
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| Challenge: | Abstractive summarization models generate fluent and well-formed output but lack semantic faithfulness, or factuality, with respect to the input documents. |
| Approach: | They propose new factuality metrics that adjust for the degree of abstractiveness . they propose to visualize the rates of change in factual as we gradually increase abstractiveity . |
| Outcome: | The proposed models generate fluent and well-formed summaries but lack semantic faithfulness, or factuality, with respect to the input documents. |
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning (2021.emnlp-main)
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| Challenge: | a proposed model for question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers is based on suggested question generation in conversational news recommendation systems. |
| Approach: | They propose a model for generating question-answer pairs with self-contained, summary-centric questions and length-constrained, article-summarizing answers. |
| Outcome: | The proposed model captures the central gists of the articles and achieves high answer accuracy. |
PLAtE: A Large-scale Dataset for List Page Web Extraction (2023.acl-industry)
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Aidan San, Yuan Zhuang, Jan Bakus, Colin Lockard, David Ciemiewicz, Sandeep Atluri, Kevin Small, Yangfeng Ji, Heba Elfardy
| Challenge: | Existing methods for web extraction are limited by the limited number of available large-scale datasets. |
| Approach: | They introduce a dataset that focuses on shopping data and a list page web extraction task. |
| Outcome: | The proposed dataset is the first large-scale list page web extraction dataset . it contains 52,898 items and 156,014 attributes, making it the first dataset based on this task . |