Search (5 results, page 1 of 1)

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Abstract

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.

Date

22. 6.2023 14:55:20

Type

a

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Abstract

On April 2, 2018, the State Council of China formally released a national Research Data Management (RDM) policy "Measures for Managing Scientific Data". In this context and given that university libraries have played an important role in supporting RDM at an institutional level in North America, Europe, and Australasia, the aim of this article is to explore the current status of RDM in Chinese universities, in particular how university libraries have been involved in taking the agenda forward. This article uses a mixed-methods data collection approach and draws on a website analysis of university policies and services; a questionnaire for university librarians; and semi-structured interviews. Findings indicate that Research Data Service at a local level in Chinese Universities are in their infancy. There is more evidence of activity in developing data repositories than support services. There is little development of local policy. Among the explanations of this may be the existence of a national-level infrastructure for some subject disciplines, the lack of professionalization of librarianship, and the relatively weak resonance of openness as an idea in the Chinese context.

Type

a

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Abstract

Each section header of an article has its distinct communicative function. Citations from distinct sections may be different regarding citing motivation. In this paper, we grouped section headers with similar functions as a structural function and defined the distribution of citations from structural functions for a paper as its citation structure. We aim to explore the relationship between citation structure and the future impact of a publication and disclose the relative importance among citations from different structural functions. Specifically, we proposed two citation counting methods and a citation life cycle identification method, by which the regression data were built. Subsequently, we employed a ridge regression model to predict the future impact of the paper and analyzed the relative weights of regressors. Based on documents collected from the Association for Computational Linguistics Anthology website, our empirical experiments disclosed that functional structure features improve the prediction accuracy of citation count prediction and that there exist differences among citations from different structural functions. Specifically, at the early stage of citation lifetime, citations from Introduction and Method are particularly important for perceiving future impact of papers, and citations from Result and Conclusion are also vital. However, early accumulation of citations from the Background seems less important.

Type

a

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Abstract

The purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.

Content

Beitrag in: JASIST special issue on 'Who tweets scientific publications? A large-scale study of tweeting audiences in all areas of research'. Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24830.

Type

a

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Abstract

This paper investigates different uses of the Journal Impact Factor (JIF) in national journal rankings and discusses the merits of supplementing metrics with expert assessment. Our focus is national journal rankings used as evidence to support decisions about the distribution of institutional funding or career advancement. The seven countries under comparison are China, Denmark, Finland, Italy, Norway, Poland, and Turkey-and the region of Flanders in Belgium. With the exception of Italy, top-tier journals used in national rankings include those classified at the highest level, or according to tier, or points implemented. A total of 3,565 (75.8%) out of 4,701 unique top-tier journals were identified as having a JIF, with 55.7% belonging to the first Journal Impact Factor quartile. Journal rankings in China, Flanders, Poland, and Turkey classify journals with a JIF as being top-tier, but only when they are in the first quartile of the Average Journal Impact Factor Percentile. Journal rankings that result from expert assessment in Denmark, Finland, and Norway regularly classify journals as top-tier outside the first quartile, particularly in the social sciences and humanities. We conclude that experts, when tasked with metric-informed journal rankings, take into account quality dimensions that are not covered by JIFs.

Type

a