# Document Similarity for Texts of Varying Lengths via Hidden Topics

arXiv: https://arxiv.org/abs/1903.10675 · HTML: https://mlsanity.com/paper/1903.10675/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity](https://mlsanity.com/paper/2111.08366/) (2021)
- [A prototype-based model for set classification](https://mlsanity.com/paper/2408.13720/) (2024)
- [Multimodal Representation Alignment for Cross-modal Information Retrieval](https://mlsanity.com/paper/2506.08774/) (2025)
- [Unsupervised Contextualized Document Representation](https://mlsanity.com/paper/2109.10509/) (2021)
- [Semantic Similarity Measure of Natural Language Text through Machine Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study Using UCGIS GIS&T Body of Knowledge](https://mlsanity.com/paper/2305.09877/) (2023)
- [Submission-Aware Reviewer Profiling for Reviewer Recommender System](https://mlsanity.com/paper/2211.04194/) (2022)

## Abstract (arXiv)

Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of incorporating domain knowledge to text matching.