Talk2Ref: A Dataset for Reference Prediction from Scientific Talks

TL;DR

This paper introduces Talk2Ref, a large-scale dataset for reference prediction from scientific talks, and demonstrates that fine-tuning models on this data improves citation prediction accuracy in spoken scientific content.

Contribution

The paper presents the first large-scale dataset for reference prediction from scientific talks and establishes baseline models, advancing research in linking spoken scientific presentations to relevant literature.

Findings

Fine-tuning on Talk2Ref improves citation prediction performance.

State-of-the-art models face challenges with long transcripts.

The dataset enables better semantic understanding of spoken scientific content.

Abstract

Scientific talks are a growing medium for disseminating research, and automatically identifying relevant literature that grounds or enriches a talk would be highly valuable for researchers and students alike. We introduce Reference Prediction from Talks (RPT), a new task that maps long, and unstructured scientific presentations to relevant papers. To support research on RPT, we present Talk2Ref, the first large-scale dataset of its kind, containing 6,279 talks and 43,429 cited papers (26 per talk on average), where relevance is approximated by the papers cited in the talk's corresponding source publication. We establish strong baselines by evaluating state-of-the-art text embedding models in zero-shot retrieval scenarios, and propose a dual-encoder architecture trained on Talk2Ref. We further explore strategies for handling long transcripts, as well as training for domain adaptation. Our results show that fine-tuning on Talk2Ref significantly improves citation prediction performance, demonstrating both the challenges of the task and the effectiveness of our dataset for learning semantic representations from spoken scientific content. The dataset and trained models are released under an open license to foster future research on integrating spoken scientific communication into citation recommendation systems.