Papers by Sourish Dasgupta
Accuracy is not enough: Evaluating Personalization in Summarizers (2023.findings-emnlp)
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| Challenge: | Existing accuracy measures cannot evaluate the degree of personalization of summarization models. |
| Approach: | They propose to use a PENS dataset to analyze the degree of personalization of ten different summarization models. |
| Outcome: | The proposed measure can evaluate the degree of personalization of summarization models using the PENS dataset. |
PerDucer: Keyphrase-Driven Personalization Inducer for Summarization from User Histories (2026.findings-acl)
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| Challenge: | Prior work reported that prepending long interaction histories to LLMs leads to unstable personalization, especially for multi-aspect documents. |
| Approach: | They propose a personalization inducer for frozen language models that maps latent preference signals to a small set of personalized keyphrases for the query document. |
| Outcome: | The proposed model outperforms the strongest history-prompting LLMs and SLMs in the PENS and OpenAI-Reddit benchmarks. |
Are Large Language Models In-Context Personalized Summarizers? Get an iCOPERNICUS Test Done! (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have succeeded in summarizing information in contexts but saliency is subject to user preferences. |
| Approach: | They propose a framework that measures saliency using user reading histories and contrast in user profiles. |
| Outcome: | The proposed framework evaluates state-of-the-art LLMs on their ICL performance and shows that they lack true ICPL. |