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Found 10 papers, 7 papers with code
Deep Keyphrase Generation
Keyphrase provides highly-condensed information that can be effectively used for understanding, organizing and retrieving text content.
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.
Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence
Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation.
An Empirical Study on Neural Keyphrase Generation
Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation
Finally, we fine-tune the model with limited data with true labels to fully adapt it to the target domain.
Integrating Transformer and Paraphrase Rules for Sentence Simplification
Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning.
Ranked #3 on
Text Simplification
on ASSET
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents
Faceted summarization provides briefings of a document from different perspectives.
Ranked #1 on
Unsupervised Extractive Summarization
on FacetSum
Enhancing Automatic ICD-9-CM Code Assignment for Medical Texts with PubMed
Frequent diseases often have more training data, which helps its classification to perform better than that of an infrequent disease.
Concept Annotation for Intelligent Textbooks
The outcomes of our work include a validated knowledge engineering procedure, a code-book for technical concept annotation, and a set of concept annotations for the target textbook, which could be used as gold standard in further research.
Effects of Different Prompts on the Quality of GPT-4 Responses to Dementia Care Questions
Evidence suggests that different prompts lead large language models (LLMs) to generate responses with varying quality.
