ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
This paper introduces a new way to summarize scientific papers by creating a large collection of annotated articles and using both the authors' main points and how often their work is cited by others.
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- 1 Summarizing scientific papers is difficult due to a lack of resources.
- 2 The authors created a large, annotated collection of papers to help with summarization.
- 3 New methods combine authors' highlights with citation data for better summaries.
- 4 Experiments show these new summaries are more effective than traditional ones.
Introduction
The introduction discusses the challenges in scientific article summarization, particularly the lack of large annotated corpora and the need for summaries that reflect the impact of articles on the research community.
Summarization Methods
The authors propose new summarization methods that combine the original highlights of the authors with citation data to create hybrid summaries that are more informative than traditional methods.
Corpus Development
This section details the development and release of a large-scale manually-annotated corpus for scientific papers in computational linguistics, highlighting the methods used to enable faster annotation.
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Experiments
Experiments are conducted to demonstrate the effectiveness of the newly developed corpus in training data-driven models for summarization and to compare the performance of hybrid summaries against traditional abstracts and citation-based summaries.
Conclusion
The conclusion summarizes the contributions of the paper, emphasizing the potential of the annotated corpus and hybrid summarization methods to advance research in scientific paper summarization.
Introduction
The introduction discusses the challenges in scientific article summarization, particularly the lack of large annotated corpora and the need for summaries that reflect the impact of articles on the research community.
Corpus Development
This section details the development and release of a large-scale manually-annotated corpus for scientific papers in computational linguistics, highlighting the methods used to enable faster annotation.
Summarization Methods
The authors propose new summarization methods that combine the original highlights of the authors with citation data to create hybrid summaries that are more informative than traditional methods.
Experiments
Experiments are conducted to demonstrate the effectiveness of the newly developed corpus in training data-driven models for summarization and to compare the performance of hybrid summaries against traditional abstracts and citation-based summaries.
Conclusion
The conclusion summarizes the contributions of the paper, emphasizing the potential of the annotated corpus and hybrid summarization methods to advance research in scientific paper summarization.
Frequently Asked Questions
This paper introduces a new way to summarize scientific papers by creating a large collection of annotated articles and using both the authors’ main points and how often their work is cited by others.
The introduction discusses the challenges in scientific article summarization, particularly the lack of large annotated corpora and the need for summaries that reflect the impact of articles on the research community.
The authors propose new summarization methods that combine the original highlights of the authors with citation data to create hybrid summaries that are more informative than traditional methods.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.