Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity

Understand

We present a new scientific document similarity model based on matching fine-grained aspects of texts.

• To train our model, we exploit a naturally-occurring source of supervision: sentences in the full-text of papers that cite multiple papers together (co-citations).
• Such co-citations not only reflect close paper relatedness, but also provide textual descriptions of how the co-cited papers are related.
• This novel form of textual supervision is used for learning to match aspects across papers.