LinkNav: Surfacing Interconnected Information in Scientific Articles
Sebastian Antony Joseph University of Texas at Austin, Jennifer Healey Adobe Systems, Junyi Jessy Li University of Texas at Austin, Ani Nenkova Adobe Research
原文摘要
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.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表