FeRoSA: A Faceted Recommendation System for Scientific Articles

Chakraborty, Tanmoy; Krishna, Amrith; Singh, Mayank; Ganguly, Niloy; Goyal, Pawan; Mukherjee, Animesh

doi:10.1007/978-3-319-31750-2_42

Cited by 24 publications

(20 citation statements)

References 18 publications

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“…The distribution of citations within the structure of scientific papers have been also studied [9]. The authors of [17] have investigated the problem of measuring the intensity of the citations in scientific papers and in [16], the authors proposed using the discourse facets for scientific article recommendation. Recently, a framework for understanding citation function has been proposed [45] which unifies all the previous efforts in terms of definition of citation functions.…”

Section: Citation Text Analysismentioning

confidence: 99%

Scientific document summarization via citation contextualization and scientific discourse

Cohan

Goharian

2017

Int J Digit Libr

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The rapid growth of scientific literature has made it difficult for the researchers to quickly learn about the developments in their respective fields. Scientific document summarization addresses this challenge by providing summaries of the important contributions of scientific papers. We present a framework for scientific summarization which takes advantage of the citations and the scientific discourse structure. Citation texts often lack the evidence and context to support the content of the cited paper and are even sometimes inaccurate. We first address the problem of inaccuracy of the citation texts by finding the relevant context from the cited paper. We propose three approaches for contextualizing citations which are based on query reformulation, word embeddings, and supervised learning. We then train a model to identify the discourse facets for each citation. We finally propose a method for summarizing scientific papers by leveraging the faceted citations and their corresponding contexts. We evaluate our proposed method on two scientific summarization datasets in the biomedical and computational linguistics domains. Extensive evaluation results show that our methods can improve over the state of the art by large margins. * This is a pre-print of an article published on IJDL. The final publication is available at Springer via http://dx.

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Section: Citation Text Analysismentioning

confidence: 99%

Scientific document summarization via citation contextualization and scientific discourse

Cohan

Goharian

2017

Int J Digit Libr

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“…The distribution of citations within the structure of scientific papers have been also studied [18]. The authors of [36] have investigated the problem of measuring the intensity of the citations in scientific papers and in [37], the authors proposed using the discourse facets for scientific article recommendation. Recently, a framework for understanding citation function has been proposed [132] which unifies all the previous efforts in terms of definition of citation functions.…”

Section: Citation Text Analysismentioning

confidence: 99%

Text Summarization and Categorization for Scientific and Health-Related Data

Cohan

2019

SIGIR Forum

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The increasing amount of unstructured health-related data has created a need for intelligent processing, summarizing, and categorizing these data to extract knowledge from them. My research goal in this dissertation is to develop Natural Language Processing (NLP) and Information Retrieval (IR) methods for better processing and understanding health-related textual information to promote health care and wellbeing of individuals.

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“…As this is a training-based approach, the model needs to be re-run whenever new papers are added. The combination of citations and textual content in formulating recommendations was adopted in another recent study (Chakraborty et al ., 2016). The recommendation technique is based on the random walk with the restart (RWR) algorithm and it classifies similar papers into different facets, such as alternate approaches, background, and methods, thereby facilitating easier understanding of the recommended papers for researchers.…”

Section: Related Workmentioning

confidence: 99%

Can I have more of these please?

Raamkumar

Foo

Pang

2018

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Purpose During the literature review phase, the task of finding similar research papers can be a difficult proposition for researchers due to the procedural complexity of the task. Current systems and approaches help in finding similar papers for a given paper, even though researchers tend to additionally search using a set of papers. This paper aims to focus on conceptualizing and developing recommendation techniques for key literature review and manuscript preparatory tasks that are interconnected. In this paper, the user evaluation results of the task where seed basket-based discovery of papers is performed are presented. Design/methodology/approach A user evaluation study was conducted on a corpus of papers extracted from the ACM Digital Library. Participants in the study included 121 researchers who had experience in authoring research papers. Participants, split into students and staff groups, had to select one of the provided 43 topics and run the tasks offered by the developed assistive system. A questionnaire was provided at the end of each task for evaluating the task performance. Findings The results show that the student group evaluated the task more favourably than the staff group, even though the difference was statistically significant for only 5 of the 16 measures. The measures topical relevance, interdisciplinarity, familiarity and usefulness were found to be significant predictors for user satisfaction in this task. A majority of the participants, who explicitly stated the need for assistance in finding similar papers, were satisfied with the recommended papers in the study. Originality/value The current research helps in bridging the gap between novices and experts in terms of literature review skills. The hybrid recommendation technique evaluated in this study highlights the effectiveness of combining the results of different approaches in finding similar papers.

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FeRoSA: A Faceted Recommendation System for Scientific Articles

Cited by 24 publications

References 18 publications

Scientific document summarization via citation contextualization and scientific discourse

Scientific document summarization via citation contextualization and scientific discourse

Text Summarization and Categorization for Scientific and Health-Related Data

Can I have more of these please?

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