Community-Driven Comprehensive Scientific Paper Summarization: Insight from cvpaper.challenge

• URL: http://arxiv.org/abs/2203.09109v1
• Date: Thu, 17 Mar 2022 06:31:17 GMT
• Title: Community-Driven Comprehensive Scientific Paper Summarization: Insight from cvpaper.challenge
• Authors: Shintaro Yamamoto, Hirokatsu Kataoka, Ryota Suzuki, Seitaro Shinagawa, Shigeo Morishima
• Abstract summary: We organized a group of non-native English speakers to write summaries of papers presented at a computer vision conference. We summarized a total of 2,000 papers presented at the Conference on Computer Vision and Pattern Recognition.
• Score: 23.10314444860379
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The present paper introduces a group activity involving writing summaries of conference proceedings by volunteer participants. The rapid increase in scientific papers is a heavy burden for researchers, especially non-native speakers, who need to survey scientific literature. To alleviate this problem, we organized a group of non-native English speakers to write summaries of papers presented at a computer vision conference to share the knowledge of the papers read by the group. We summarized a total of 2,000 papers presented at the Conference on Computer Vision and Pattern Recognition, a top-tier conference on computer vision, in 2019 and 2020. We quantitatively analyzed participants' selection regarding which papers they read among the many available papers. The experimental results suggest that we can summarize a wide range of papers without asking participants to read papers unrelated to their interests.

Related papers

• SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
  This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
  arXiv  Detail & Related papers  (2024-04-09T15:01:51Z)
• PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers [40.01511301396072]
  PaperWeaver is an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. Our user study showed that participants using PaperWeaver were able to better understand the relevance of recommended papers.
  arXiv  Detail & Related papers  (2024-03-05T13:10:06Z)
• On the Detection of Reviewer-Author Collusion Rings From Paper Bidding [71.43634536456844]
  Collusion rings pose a major threat to the peer-review systems of computer science conferences. One approach to solve this problem would be to detect the colluding reviewers from their manipulated bids. No research has yet established that detecting collusion rings is even possible.
  arXiv  Detail & Related papers  (2024-02-12T18:12:09Z)
• Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
  We investigate the role of social media influencers in enhancing the visibility of machine learning research. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
  arXiv  Detail & Related papers  (2024-01-24T20:05:49Z)
• "Why Should I Review This Paper?" Unifying Semantic, Topic, and Citation Factors for Paper-Reviewer Matching [31.658757187200603]
  We propose a unified model for paper-reviewer matching that jointly captures semantic, topic, and citation factors. Experiments on four datasets consistently validate our proposed UniPR model in comparison with state-of-the-art paper-reviewer matching methods.
  arXiv  Detail & Related papers  (2023-10-23T01:29:18Z)
• Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation [55.00687185394986]
  We propose the task of scientific opinion summarization, where research paper reviews are synthesized into meta-reviews. We introduce the ORSUM dataset covering 15,062 paper meta-reviews and 57,536 paper reviews from 47 conferences. Our experiments show that (1) human-written summaries do not always satisfy all necessary criteria such as depth of discussion, and identifying consensus and controversy for the specific domain, and (2) the combination of task decomposition and iterative self-refinement shows strong potential for enhancing the opinions.
  arXiv  Detail & Related papers  (2023-05-24T02:33:35Z)
• MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents [77.34726150561087]
  We propose MIReAD, a simple method that learns high-quality representations of scientific papers. We train MIReAD on more than 500,000 PubMed and arXiv abstracts across over 2,000 journal classes.
  arXiv  Detail & Related papers  (2023-05-07T03:29:55Z)
• The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces [54.2590226904332]
  We describe the Semantic Reader Project, a effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers. Ten prototype interfaces have been developed and more than 300 participants and real-world users have shown improved reading experiences. We structure this paper around challenges scholars and the public face when reading research papers.
  arXiv  Detail & Related papers  (2023-03-25T02:47:09Z)
• Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing [36.60598912719588]
  We introduce a novel interactive interface-Paper Plain-with four features powered by natural language processing. definitions of unfamiliar terms, in-situ plain language section summaries, and plain language summaries of the answering passages. We find that participants who use Paper Plain have an easier time reading and understanding research papers without a loss in paper comprehension compared to those who use a typical PDF reader.
  arXiv  Detail & Related papers  (2022-02-28T22:59:21Z)
• A Correspondence Analysis Framework for Author-Conference Recommendations [2.1055643409860743]
  We use Correspondence Analysis (CA) to derive appropriate relationships between the entities in question, such as conferences and papers. Our models show promising results when compared with existing methods such as content-based filtering, collaborative filtering and hybrid filtering.
  arXiv  Detail & Related papers  (2020-01-08T18:52:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.