Summary of Pre-trained Language Models For Keyphrase Prediction: a Review, by Muhammad Umair et al.
Pre-Trained Language Models for Keyphrase Prediction: A Review
by Muhammad Umair, Tangina Sultana, Young-Koo Lee
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the use of pre-trained language models for keyphrase prediction (PLM-KP), a crucial task in natural language processing (NLP). It discusses the limitations of previous surveys on KP and highlights the need for a unified analysis. The authors examine PLM-KP, which uses different learning techniques such as supervised, unsupervised, semi-supervised, and self-supervised methods to train models on large text corpora. They introduce taxonomies for keyphrase extraction (KPE) and keyphrase generation (KPG), two main tasks in NLP. The paper also suggests promising future directions for predicting keyphrases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how computers can help us understand the most important ideas in a piece of writing. It’s like finding the summary of an article or book. Right now, there are many ways to do this using special computer programs. The authors of this paper want to study these different methods and figure out which ones work best. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Self supervised » Semi supervised » Supervised » Unsupervised
