# Automatic Labelling of Topics with Neural Embeddings

arXiv: https://arxiv.org/abs/1612.05340 · HTML: https://mlsanity.com/paper/1612.05340/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2016.

## Built on by (most cited first, 12 of 12)

- [The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles](https://mlsanity.com/paper/2104.00948/) (2021)
- [Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model](https://mlsanity.com/paper/2109.10033/) (2021)
- [Coordinated Topic Modeling](https://mlsanity.com/paper/2210.08559/) (2022)
- [Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews](https://mlsanity.com/paper/2101.10150/) (2021)
- [TopicAdapt- An Inter-Corpora Topics Adaptation Approach](https://mlsanity.com/paper/2310.04978/) (2023)
- [Topic Modelling: Going Beyond Token Outputs](https://mlsanity.com/paper/2401.12990/) (2024)
- [Open-domain Topic Identification of Out-of-domain Utterances using Wikipedia](https://mlsanity.com/paper/2101.11134/) (2021)
- [Automatic Scene-based Topic Channel Construction System for E-Commerce](https://mlsanity.com/paper/2210.02643/) (2022)
- [Towards an automatic method for generating topical vocabulary test forms for specific reading passages](https://mlsanity.com/paper/2505.18762/) (2025)
- [Efficient Topic Extraction via Graph-Based Labeling: A Lightweight Alternative to Deep Models](https://mlsanity.com/paper/2511.04248/) (2025)
- [How Much Structure Do LLMs Need? Evaluating LLMs for Bibliometric Cluster Description](https://mlsanity.com/paper/2605.24351/) (2026)
- [Label Semantic Expansion via Label Guided Neural Topic Modeling](https://mlsanity.com/paper/2608.30216/) (2026)

## Abstract (arXiv)

Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Compared to a state-of-the-art topic labelling system, our methodology is simpler, more efficient, and finds better topic labels.