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Relevant publications in YOUR_DOMAIN

Student: Name Surname

Publication Title

Published: 01-31-2021 (month-day-year)

Paper: www.paper_website.com

Venue: Conference XYZ

Code:

Dataset: www.dataset_website.com

Highlights:

  • This paper introduces a new technique ...
  • It releases a bechmark data collection...
  • It it interesting because...
  • The results are computed using...
  • The architecture leverages on...

Abstract:

Bibtex:

bibtex citation

Extracting highlights of scientific articles: A supervised summarization approach

Published: 12-01-2020

Paper: https://www.sciencedirect.com/science/article/abs/pii/S0957417420304838

Venue: Journal Expert Systems with Applications

Code: https://github.com/MorenoLaQuatra/domain-specific-academic-dataset

Dataset: https://github.com/MorenoLaQuatra/domain-specific-academic-dataset

Abstract:

Scientific articles can be annotated with short sentences, called highlights, providing readers with an at-a-glance overview of the main findings. Highlights are usually manually specified by the authors. This paper presents a supervised approach, based on regression techniques, with the twofold aim at automatically extracting highlights of past articles with missing annotations and simplifying the process of manually annotating new articles. To this end, regression models are trained on a variety of features extracted from previously annotated articles. The proposed approach extends existing extractive approaches by predicting a similarity score, based on n-gram co-occurrences, between article sentences and highlights. The experimental results, achieved on a benchmark collection of articles ranging over heterogeneous topics, show that the proposed regression models perform better than existing methods, both supervised and not.

Highlights:

  • This paper introduces a new technique for the extraction of highlights of research papers.
  • The main method relies on supervised machine learning algorithm.
  • It evaluates using ROUGE-based metrics.
  • The paper releases two benchmark datasets for the AI and BioMedical domains.
  • The approach uses domain-specific features to train a regression model.
@article{CAGLIERO2020113659,
  title = "Extracting highlights of scientific articles: A supervised summarization approach",
  journal = "Expert Systems with Applications",
  volume = "160",
  pages = "113659",
  year = "2020",
  issn = "0957-4174",
  doi = "https://doi.org/10.1016/j.eswa.2020.113659",
  url = "http://www.sciencedirect.com/science/article/pii/S0957417420304838",
  author = "Luca Cagliero and Moreno {La Quatra}",
  keywords = "Highlight extraction, Extractive summarization, Regression models, Text mining and analytics"
}