# Query-Based Keyphrase Extraction from Long Documents

arXiv: https://arxiv.org/abs/2205.05391 · HTML: https://mlsanity.com/paper/2205.05391/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 1 of 1)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)

## Built on by (most cited first, 4 of 4)

- [ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task](https://mlsanity.com/paper/2304.14177/) (2023)
- [Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings](https://mlsanity.com/paper/2305.09316/) (2023)
- [SlovKE: A Large-Scale Dataset and LLM Evaluation for Slovak Keyphrase Extraction](https://mlsanity.com/paper/2603.15523/) (2026)
- [Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings](https://mlsanity.com/paper/2606.10716/) (2026)

## Abstract (arXiv)

Transformer-based architectures in natural language processing force input size limits that can be problematic when long documents need to be processed. This paper overcomes this issue for keyphrase extraction by chunking the long documents while keeping a global context as a query defining the topic for which relevant keyphrases should be extracted. The developed system employs a pre-trained BERT model and adapts it to estimate the probability that a given text span forms a keyphrase. We experimented using various context sizes on two popular datasets, Inspec and SemEval, and a large novel dataset. The presented results show that a shorter context with a query overcomes a longer one without the query on long documents.