@inproceedings{lee-etal-2026-summaries,
title = "When Summaries Distort Decisions: Information Fidelity in {LLM}-Compressed Financial Analysis",
author = "Lee, Hoyoung and
Park, Suhwan and
Lee, Seunghan and
Seo, Jun and
Lee, Jaehoon and
Yoo, Sungdong and
Kim, Minjae and
Na, CheolWon and
Wang, Zhangyang and
Golkhou, Zach and
Kim, Minkyu and
Sabanis, Sotirios and
Lopez-Lira, Alejandro and
Mehta, Dhagash and
Lee, Soonyoung and
Choi, Chanyeol and
Ahn, Wonbin and
Lee, Yongjae",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.232/",
pages = "3870--3887",
ISBN = "979-8-89176-460-6",
abstract = "Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial source material, they can alter the investment judgment supported by the original source. We frame this problem as information fidelity: compression loses fidelity when it changes the decision induced by the source. In agentic systems, such losses may recur across intermediate steps and amplify throughout the decision process. Across financial filings and earnings-call transcripts, we find that LLM-based compression can produce fluent and factually plausible compressed contexts that nevertheless alter downstream decisions. We analyze two diagnostic patterns associated with fidelity loss: decontextualization, where salient evidence is retained but separated from the caveats and contextual qualifiers needed for correct interpretation, and model dependency, where different compressors expose different views of the same source. We then propose Agentic Context Compression, which generates multiple candidate compressions and audits their disagreements against the original source. Our results suggest that financial compression should be evaluated not only by efficiency or factuality, but also by its ability to preserve decision-relevant context."
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<abstract>Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial source material, they can alter the investment judgment supported by the original source. We frame this problem as information fidelity: compression loses fidelity when it changes the decision induced by the source. In agentic systems, such losses may recur across intermediate steps and amplify throughout the decision process. Across financial filings and earnings-call transcripts, we find that LLM-based compression can produce fluent and factually plausible compressed contexts that nevertheless alter downstream decisions. We analyze two diagnostic patterns associated with fidelity loss: decontextualization, where salient evidence is retained but separated from the caveats and contextual qualifiers needed for correct interpretation, and model dependency, where different compressors expose different views of the same source. We then propose Agentic Context Compression, which generates multiple candidate compressions and audits their disagreements against the original source. Our results suggest that financial compression should be evaluated not only by efficiency or factuality, but also by its ability to preserve decision-relevant context.</abstract>
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%0 Conference Proceedings
%T When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
%A Lee, Hoyoung
%A Park, Suhwan
%A Lee, Seunghan
%A Seo, Jun
%A Lee, Jaehoon
%A Yoo, Sungdong
%A Kim, Minjae
%A Na, CheolWon
%A Wang, Zhangyang
%A Golkhou, Zach
%A Kim, Minkyu
%A Sabanis, Sotirios
%A Lopez-Lira, Alejandro
%A Mehta, Dhagash
%A Lee, Soonyoung
%A Choi, Chanyeol
%A Ahn, Wonbin
%A Lee, Yongjae
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F lee-etal-2026-summaries
%X Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial source material, they can alter the investment judgment supported by the original source. We frame this problem as information fidelity: compression loses fidelity when it changes the decision induced by the source. In agentic systems, such losses may recur across intermediate steps and amplify throughout the decision process. Across financial filings and earnings-call transcripts, we find that LLM-based compression can produce fluent and factually plausible compressed contexts that nevertheless alter downstream decisions. We analyze two diagnostic patterns associated with fidelity loss: decontextualization, where salient evidence is retained but separated from the caveats and contextual qualifiers needed for correct interpretation, and model dependency, where different compressors expose different views of the same source. We then propose Agentic Context Compression, which generates multiple candidate compressions and audits their disagreements against the original source. Our results suggest that financial compression should be evaluated not only by efficiency or factuality, but also by its ability to preserve decision-relevant context.
%U https://aclanthology.org/2026.emnlp-industry.232/
%P 3870-3887
Markdown (Informal)
[When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis](https://aclanthology.org/2026.emnlp-industry.232/) (Lee et al., EMNLP 2026)
ACL
- Hoyoung Lee, Suhwan Park, Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, CheolWon Na, Zhangyang Wang, Zach Golkhou, Minkyu Kim, Sotirios Sabanis, Alejandro Lopez-Lira, Dhagash Mehta, Soonyoung Lee, Chanyeol Choi, Wonbin Ahn, and Yongjae Lee. 2026. When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3870–3887, Budapest, Hungary. Association for Computational Linguistics.