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

• URL: http://arxiv.org/abs/2111.08366v1
• Date: Tue, 16 Nov 2021 11:12:30 GMT
• Title: Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
• Authors: Sheshera Mysore, Arman Cohan, Tom Hope
• Abstract summary: We present a new scientific document similarity model based on matching fine-grained aspects. Our model is trained using co-citation contexts that describe related paper aspects as a novel form of textual supervision.
• Score: 11.157086694203201
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Aspire, a new scientific document similarity model based on matching fine-grained aspects. Our model is trained using co-citation contexts that describe related paper aspects as a novel form of textual supervision. We use multi-vector document representations, recently explored in settings with short query texts but under-explored in the challenging document-document setting. We present a fast method that involves matching only single sentence pairs, and a method that makes sparse multiple matches with optimal transport. Our model improves performance on document similarity tasks across four datasets. Moreover, our fast single-match method achieves competitive results, opening up the possibility of applying fine-grained document similarity models to large-scale scientific corpora.

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