# Traceable Text: Deepening Reading of AI-Generated Summaries with Phrase-Level Provenance Links

arXiv: https://arxiv.org/abs/2409.13099 · HTML: https://mlsanity.com/paper/2409.13099/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2024.

## Built on by (most cited first, 4 of 4)

- [TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain](https://mlsanity.com/paper/2508.13798/) (2025)
- [Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers](https://mlsanity.com/paper/2510.00361/) (2025)
- [GenProve: Learning to Generate Text with Fine-Grained Provenance](https://mlsanity.com/paper/2601.04932/) (2026)
- [From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization](https://mlsanity.com/paper/2601.16397/) (2026)

## Abstract (arXiv)

As AI-generated summaries proliferate, how can we help people understand the veracity of those summaries? In this short paper, we design a simple interaction primitive, traceable text, to support critical examination of generated summaries and the source texts they were derived from. In a traceable text, passages of a generated summary link to passages of the source text that informed them. A traceable text can be generated with a straightforward prompt chaining approach, and optionally adjusted by human authors depending on application. In a usability study, we examined the impact of traceable texts on reading and understanding patient medical records. Traceable text helped readers answer questions about the content of the source text more quickly and markedly improved correctness of answers in cases where there were hallucinations in the summaries. When asked to read a text of personal importance with traceable text, readers employed traceable text as an understanding aid and as an index into the source note.