Papers by Aishik Nagar
How do Transformer Embeddings Represent Compositions? A Functional Analysis (2025.findings-acl)
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| Challenge: | Despite the popularity of transformer-based models, little is known about how they represent compound words and whether they are compositional. |
| Approach: | They evaluate compositionality in mistral, OpenAI Large, and Google embedding models and compare them with BERT. |
| Outcome: | The proposed models perform best in addition, multiplication, dilation, regression, and the classic vector addition model performs almost as well as any other model. |
uMedSum: A Unified Framework for Clinical Abstractive Summarization (2025.acl-long)
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Aishik Nagar, Yutong Liu, Andy T. Liu, Viktor Schlegel, Vijay Prakash Dwivedi, Arun-Kumar Kaliya-Perumal, Guna Pratheep Kalanchiam, Yili Tang, Robby T. Tan
| Challenge: | Clinical abstractive summarization struggles to balance faithfulness and informativeness, sacrificing key information or introducing confabulations. |
| Approach: | They develop a modular hybrid framework that integrates confabulation removal and key information addition into abstractive summarization methods. |
| Outcome: | The proposed framework outperforms state-of-the-art abstractive summarization methods in both quantitative metrics and expert evaluations. |