@inproceedings{singh-etal-2025-fidelity,
title = "{FIDELITY}: Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding",
author = "Singh, Divyansh and
Mather, Brodie and
Zhang, Demi and
Lehman, Patrick and
Ho, Justin and
Dorr, Bonnie J",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.132/",
doi = "10.18653/v1/2025.findings-naacl.132",
pages = "2460--2472",
ISBN = "979-8-89176-195-7",
abstract = "The rapid expansion of text data has increased the need for effective methods to distill meaningful information from large datasets. Traditional and state-of-the-art approaches have made significant strides in topic modeling, yet they fall short in generating contextually specific and semantically intuitive topics, particularly in dynamic environments and low-resource languages. Additionally, multi-document summarization systems often struggle with issues like redundancy, scalability, and maintaining readability. We introduce FIDELITY (Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding), a hybrid method that combines topic modeling and text summarization to produce fine-grained, semantically rich, and contextually relevant output. FIDELITY enhances dataset accessibility and interpretability, outperforming traditional models in topic diversity, similarity, and in the ability to process new, unseen documents. Additionally, it demonstrates robust multilingual capabilities, effectively handling low-resource languages like Tagalog. This makes FIDELITY a powerful tool for distilling and understanding complex textual data, providing detailed insights while maintaining the necessary granularity for practical applications."
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<abstract>The rapid expansion of text data has increased the need for effective methods to distill meaningful information from large datasets. Traditional and state-of-the-art approaches have made significant strides in topic modeling, yet they fall short in generating contextually specific and semantically intuitive topics, particularly in dynamic environments and low-resource languages. Additionally, multi-document summarization systems often struggle with issues like redundancy, scalability, and maintaining readability. We introduce FIDELITY (Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding), a hybrid method that combines topic modeling and text summarization to produce fine-grained, semantically rich, and contextually relevant output. FIDELITY enhances dataset accessibility and interpretability, outperforming traditional models in topic diversity, similarity, and in the ability to process new, unseen documents. Additionally, it demonstrates robust multilingual capabilities, effectively handling low-resource languages like Tagalog. This makes FIDELITY a powerful tool for distilling and understanding complex textual data, providing detailed insights while maintaining the necessary granularity for practical applications.</abstract>
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%0 Conference Proceedings
%T FIDELITY: Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding
%A Singh, Divyansh
%A Mather, Brodie
%A Zhang, Demi
%A Lehman, Patrick
%A Ho, Justin
%A Dorr, Bonnie J.
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F singh-etal-2025-fidelity
%X The rapid expansion of text data has increased the need for effective methods to distill meaningful information from large datasets. Traditional and state-of-the-art approaches have made significant strides in topic modeling, yet they fall short in generating contextually specific and semantically intuitive topics, particularly in dynamic environments and low-resource languages. Additionally, multi-document summarization systems often struggle with issues like redundancy, scalability, and maintaining readability. We introduce FIDELITY (Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding), a hybrid method that combines topic modeling and text summarization to produce fine-grained, semantically rich, and contextually relevant output. FIDELITY enhances dataset accessibility and interpretability, outperforming traditional models in topic diversity, similarity, and in the ability to process new, unseen documents. Additionally, it demonstrates robust multilingual capabilities, effectively handling low-resource languages like Tagalog. This makes FIDELITY a powerful tool for distilling and understanding complex textual data, providing detailed insights while maintaining the necessary granularity for practical applications.
%R 10.18653/v1/2025.findings-naacl.132
%U https://aclanthology.org/2025.findings-naacl.132/
%U https://doi.org/10.18653/v1/2025.findings-naacl.132
%P 2460-2472
Markdown (Informal)
[FIDELITY: Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding](https://aclanthology.org/2025.findings-naacl.132/) (Singh et al., Findings 2025)
ACL