# Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings

arXiv: https://arxiv.org/abs/1807.05962 · HTML: https://mlsanity.com/paper/1807.05962/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Phraseformer: Multimodal Key-phrase Extraction using Transformer and Graph Embedding](https://mlsanity.com/paper/2106.04939/) (2021)
- [Keyword Extraction in Scientific Documents](https://mlsanity.com/paper/2207.01888/) (2022)
- [Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings](https://mlsanity.com/paper/2305.09316/) (2023)
- [EntropyRank: Unsupervised Keyphrase Extraction via Side-Information Optimization for Language Model-based Text Compression](https://mlsanity.com/paper/2308.13399/) (2023)
- [Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings](https://mlsanity.com/paper/2606.10716/) (2026)

## Abstract (arXiv)

Keyword extraction is a fundamental task in natural language processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases. Keywords from text documents are primarily extracted using supervised and unsupervised approaches. In this paper, we present an unsupervised technique that uses a combination of theme-weighted personalized PageRank algorithm and neural phrase embeddings for extracting and ranking keywords. We also introduce an efficient way of processing text documents and training phrase embeddings using existing techniques. We share an evaluation dataset derived from an existing dataset that is used for choosing the underlying embedding model. The evaluations for ranked keyword extraction are performed on two benchmark datasets comprising of short abstracts (Inspec), and long scientific papers (SemEval 2010), and is shown to produce results better than the state-of-the-art systems.