@inproceedings{joseph-etal-2026-linknav,
title = "{L}ink{N}av: Surfacing Interconnected Information in Scientific Articles",
author = "Joseph, Sebastian Antony and
Healey, Jennifer and
Li, Junyi Jessy and
Nenkova, Ani",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.45/",
pages = "453--462",
ISBN = "979-8-89176-392-0",
abstract = "We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we instruct a language model to generate questions that may arise while reading a passage and then search for answer-bearing passages elsewhere in the document, forming intra-document connections when answers are found. We confirm that these building blocks work well to power the experience, with an answer detection pipeline that works with high precision, resulting in a reasonable number of such connections being made for a document. On a dataset of academic papers, we find that connected segments are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed."
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<abstract>We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we instruct a language model to generate questions that may arise while reading a passage and then search for answer-bearing passages elsewhere in the document, forming intra-document connections when answers are found. We confirm that these building blocks work well to power the experience, with an answer detection pipeline that works with high precision, resulting in a reasonable number of such connections being made for a document. On a dataset of academic papers, we find that connected segments are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed.</abstract>
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%0 Conference Proceedings
%T LinkNav: Surfacing Interconnected Information in Scientific Articles
%A Joseph, Sebastian Antony
%A Healey, Jennifer
%A Li, Junyi Jessy
%A Nenkova, Ani
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F joseph-etal-2026-linknav
%X We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we instruct a language model to generate questions that may arise while reading a passage and then search for answer-bearing passages elsewhere in the document, forming intra-document connections when answers are found. We confirm that these building blocks work well to power the experience, with an answer detection pipeline that works with high precision, resulting in a reasonable number of such connections being made for a document. On a dataset of academic papers, we find that connected segments are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed.
%U https://aclanthology.org/2026.acl-demo.45/
%P 453-462
Markdown (Informal)
[LinkNav: Surfacing Interconnected Information in Scientific Articles](https://aclanthology.org/2026.acl-demo.45/) (Joseph et al., ACL 2026)
ACL
- Sebastian Antony Joseph, Jennifer Healey, Junyi Jessy Li, and Ani Nenkova. 2026. LinkNav: Surfacing Interconnected Information in Scientific Articles. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 453–462, San Diego, California, United States. Association for Computational Linguistics.