@inproceedings{chitale-etal-2026-graph,
title = "Graph-Guided Passage Retrieval for Author-Centric Structured Feedback",
author = "Chitale, Maitreya Prafulla and
Shetye, Ketaki Mangesh and
Gupta, Harshit and
Chaudhary, Manav and
Shrivastava, Manish and
Varma, Vasudeva",
editor = "T.Y.S.S., Santosh and
Agarwal, Amit and
Gupta, Vivek and
Kementchedjhieva, Yova and
Garimella, Aparna and
Hou, Yufang",
booktitle = "The Proceedings of Workshop on Document Intelligence and Understanding",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.docinsights-1.7/",
pages = "81--99",
ISBN = "979-8-89176-477-4",
abstract = "Obtaining high-quality, pre-submission feedback is a critical bottleneck in the academic publication lifecycle for researchers. We introduce AutoRev, an automated author-centric feedback system that generates structured, actionable guidance prior to formal peer review. AutoRev employs a graph-based retrieval-augmented generation framework that models each paper as a hierarchical document graph, integrating textual and structural representations to retrieve salient content efficiently. By leveraging graph-based passage retrieval, AutoRev substantially reduces LLM input context length, leading to higher-quality feedback generation. Experimental results demonstrate that AutoRev significantly outperforms baselines across multiple automatic evaluation metrics, while achieving strong performance in human evaluations. We have made our code publicly available."
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<abstract>Obtaining high-quality, pre-submission feedback is a critical bottleneck in the academic publication lifecycle for researchers. We introduce AutoRev, an automated author-centric feedback system that generates structured, actionable guidance prior to formal peer review. AutoRev employs a graph-based retrieval-augmented generation framework that models each paper as a hierarchical document graph, integrating textual and structural representations to retrieve salient content efficiently. By leveraging graph-based passage retrieval, AutoRev substantially reduces LLM input context length, leading to higher-quality feedback generation. Experimental results demonstrate that AutoRev significantly outperforms baselines across multiple automatic evaluation metrics, while achieving strong performance in human evaluations. We have made our code publicly available.</abstract>
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%0 Conference Proceedings
%T Graph-Guided Passage Retrieval for Author-Centric Structured Feedback
%A Chitale, Maitreya Prafulla
%A Shetye, Ketaki Mangesh
%A Gupta, Harshit
%A Chaudhary, Manav
%A Shrivastava, Manish
%A Varma, Vasudeva
%Y T.Y.S.S., Santosh
%Y Agarwal, Amit
%Y Gupta, Vivek
%Y Kementchedjhieva, Yova
%Y Garimella, Aparna
%Y Hou, Yufang
%S The Proceedings of Workshop on Document Intelligence and Understanding
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-477-4
%F chitale-etal-2026-graph
%X Obtaining high-quality, pre-submission feedback is a critical bottleneck in the academic publication lifecycle for researchers. We introduce AutoRev, an automated author-centric feedback system that generates structured, actionable guidance prior to formal peer review. AutoRev employs a graph-based retrieval-augmented generation framework that models each paper as a hierarchical document graph, integrating textual and structural representations to retrieve salient content efficiently. By leveraging graph-based passage retrieval, AutoRev substantially reduces LLM input context length, leading to higher-quality feedback generation. Experimental results demonstrate that AutoRev significantly outperforms baselines across multiple automatic evaluation metrics, while achieving strong performance in human evaluations. We have made our code publicly available.
%U https://aclanthology.org/2026.docinsights-1.7/
%P 81-99
Markdown (Informal)
[Graph-Guided Passage Retrieval for Author-Centric Structured Feedback](https://aclanthology.org/2026.docinsights-1.7/) (Chitale et al., DocInsights 2026)
ACL
- Maitreya Prafulla Chitale, Ketaki Mangesh Shetye, Harshit Gupta, Manav Chaudhary, Manish Shrivastava, and Vasudeva Varma. 2026. Graph-Guided Passage Retrieval for Author-Centric Structured Feedback. In The Proceedings of Workshop on Document Intelligence and Understanding, pages 81–99, Budapest, Hungary. Association for Computational Linguistics.