LinkNav: Surfacing Interconnected Information in Scientific Articles

2026-06-04

AI Summary

This study addresses the challenge that implicit semantic connections between non-adjacent paragraphs in academic papers often elude readers. To tackle this issue, the authors propose a novel intra-document long-range semantic linking method grounded in a question-answering framework. Specifically, the approach leverages large language models to generate plausible questions from a given paragraph and retrieves corresponding answer paragraphs within the same paper, thereby explicitly establishing cross-paragraph semantic links. As the first work to implement such intra-paper semantic connections, this method achieves high precision in linking passages separated on average by ten paragraphs on standard benchmarks, significantly enhancing readers’ information retrieval efficiency and overall comprehension experience.

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 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 connections being made for a document. On a dataset of academic papers, we find that connected passages are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed.

Problem

• scientific articles
• intra-document connections
• non-adjacent passages
• information linking
• reading experience

Innovation

• intra-document linking
• question generation
• answer retrieval
• scientific article navigation
• language model augmentation