LongKey: Keyphrase Extraction for Long Documents
Authors: Jeovane Honorio Alves, Radu State, Cinthia Obladen de Almendra Freitas, Jean Paul Barddal


Reviewer paper summaries: Automated keyphrase extraction offers a solution by identifying pertinent and representative terms within texts. Yet, prevailing methods predominantly cater to shorter documents, often limited to 512 tokens or less, thus leaving a gap in effectively processing more extensive, long-context documents. Addressing this challenge, the author introduces LongKey, a novel keyphrase extraction framework tailored for lengthy documents, which leverages an encoder-based language model to harness the intricacies of extended texts. LongKey incorporates a suite of carefully designed components optimized for long-context analysis. Validated on the comprehensive LDKP datasets and further tested across six diverse, unseen datasets, LongKey consistently outperforms existing unsupervised and language model-based keyphrase extraction methods. The author's findings highlight LongKey’s versatility and superior performance, marking a significant advancement in automated keyphrase extraction for various text lengths and domains.
Summary of strengths: 1. LongKey is specifically designed for long documents and can process texts up to 96K tokens, which is rare among existing methods.
2. Using the Longformer model, LongKey effectively captures complex information in long contexts, which is crucial for keyphrase extraction in long documents.
3. LongKey has been tested on diverse datasets, demonstrating its generality and robustness.
4. It consistently outperforms existing methods across various tests, proving its applicability in multiple scenarios.
5. The paper is well-structured and divided into sections such as abstract, introduction, method, experiments, results, and conclusion, with a logical flow.
6. Each section is detailed, especially the methodology part, which thoroughly explains the technical details of LongKey.
7. Missing cites many keyphrase extraction studies.
Summary of weaknesses: 1. Due to the need to process many tokens, LongKey demands significant computational resources, which might be challenging for users with limited computing capabilities.
2. Involves multi-layer encoders and complex embedding representations, increasing the difficulty of implementation and debugging.
3. It relies on large-scale pre-trained models like Longformer, which might make achieving the same effects in resource-constrained environments difficult.
4. Pre-trained models might carry specific biases, affecting performance in particular domains or datasets.
Comments, suggestions and typos: see Weaknesses
Overall assessment scores: 2