• Digital Humanities and Cultural Heritage
• 2017-2021close
• Article close
• Scientometrics close

Intriguing unforced regularities in human behaviors have been reported in varied research domains, including scientometrics. In this study we examine the manuscript submission behavior of researchers, with a focus on its monthly pattern. With a large and reliable dataset which records the submission history of articles published on 10 multidisciplinary journals and 10 management journals over a five-year period (2013-2017), we observe a prominent turn-of-the-month submission effect for accepted papers in management journals but not multidisciplinary journals. This effect gets more pronounced in submissions to top-tier journals and when the first day of a month happens to be a Saturday or Sunday. Sense of ceremony is proposed as a likely explanation of this effect, since the first day of a month is a fundamental temporal landmark which has a 'fresh start effect' on researchers. To conclude, an original and interesting day-of-the-month effect in the academia is reported in this study, which calls for more research attention.

Keywords serving a dense summary of documents, are widely used in search engine and library to do information retrieval, content classification, speech recognition and automated text summarization. However, massive documents are lack of keywords, and the rapid generation of the large amount of content every day makes the human annotation really time-consuming. Lots of researches show that network-based approaches have remarkable performance for extracting text keywords. Traditionally, words are connected based upon their occurrence in documents. One recent work shows the significant influence of sentences on keywords extraction beyond the traditional methods only considering words. While in addition to words and sentences, chapters are the essential parts that are organized as the higher level semantic logic of the documents. Inspired by this idea, we therefore assume that chapters should contribute to the keyword extraction too. We further add the chapter factor to build a three-layer network model and propose a Word-Sentence-Chapter network-based approach for keywords extraction. Two experiments with Chinese and English documents respectively indicate that our approach outperforms the state of arts.

The ever-increasing number of published scientific articles has prompted the need for automated, data-driven approaches to summarizing the content of scientific articles. The Computational Linguistics Scientific Document Summarization Shared Task (CL-SciSumm 2019) has recently fostered the study and development of new text mining and machine learning solutions to the summarization problem customized to the academic domain. In CL-SciSumm, a Reference Paper (RP) is associated with a set of Citing Papers (CPs), all containing citations to the RP. In each CP, the text spans (i.e., citances) have been identified that pertain to a particular citation to the RP. The task of identifying the spans of text in the RP that most accurately reflect the citance is addressed using supervised approaches. This paper proposes a new, more effective solution to the CL-SciSumm discourse facet classification task, which entails identifying for each cited text span what facet of the paper it belongs to from a predefined set of facets. It proposes also to extend the set of traditional CL-SciSumm tasks with a new one, namely the discourse facet summarization task. The idea behind is to extract facet-specific descriptions of each RP consisting of a fixed-length collection of RP’s text spans. To tackle both the standard and the new tasks, we propose machine learning supported solutions based on the extraction of a selection of discriminating words, called pivot words. Predictive features based on pivot words are shown to be of great importance to rate the pertinence and relevance of a text span to a given facet. The newly proposed facet classification method performs significantly better than the best performing CL-SciSumm 2019 participant (i.e., the classification accuracy has increased by + 8%), whereas regression methods achieved promising results for the newly proposed summarization task.

AbstractWord embedding, which represents individual words with semantically fixed-length vectors, has made it possible to successfully apply deep learning to natural language processing tasks such as semantic role-modeling, question answering, and machine translation. As math text consists of natural text, as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding techniques can also be applied to math documents. However, while mathematics is a precise and accurate science, it is usually expressed through imprecise and less accurate descriptions, contributing to the relative dearth of machine learning applications for information retrieval in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in word embedding, it is worthwhile to explore their use and effectiveness in math information retrieval tasks, such as math language processing and semantic knowledge extraction. In this paper, we explore math embedding by testing it on several different scenarios, namely, (1) math-term similarity, (2) analogy, (3) numerical concept-modeling based on the centroid of the keywords that characterize a concept, (4) math search using query expansions, and (5) semantic extraction, i.e., extracting descriptive phrases for math expressions. Due to the lack of benchmarks, our investigations were performed using the arXiv collection of STEM documents and carefully selected illustrations on the Digital Library of Mathematical Functions (DLMF: NIST digital library of mathematical functions. Release 1.0.20 of 2018-09-1, 2018). Our results show that math embedding holds much promise for similarity, analogy, and search tasks. However, we also observed the need for more robust math embedding approaches. Moreover, we explore and discuss fundamental issues that we believe thwart the progress in mathematical information retrieval in the direction of machine learning.

AbstractFrom a historical point of view, Rome and especially the University of La Sapienza, are closely linked to two geniuses of Baroque art: Bernini and Borromini. In this study, we analyze the rivalry between them from a scientometric perspective. This study also serves as a basis for exploring which data sources may be appropriate for broad impact assessment of individuals and/or celebrities. We pay special attention to encyclopaedias, library catalogues and other databases or types of publications that are not normally used for this purpose. The results show that some sources such as Wikipedia are not exploited according to the possibilities they offer, especially those related to different languages and cultures. Moreover, analyses are often reduced to a minimum number of data sources, which can distort the relevance of the outcome. Our results show that other sources normally not considered for this purpose, like JSTOR, PQDT, Google Scholar, Catalogue Holdings, etc. can provide more relevant or abundant information than the typically used Web of Science Core Collection and Scopus. Finally, we also contrast opportunities and limitation of old and new (YouTube, Twitter) data sources (particularly the aspects quality and accuracy of the search methods). Much room for improvement has been identified in order to use data sources more efficiently and with higher accuracy.

I examine the citation patterns to a revolutionary scientific paper, Hess’ “History of Ocean Basins”, which played a significant role in the plate tectonics revolution in the geosciences. I test two predictions made by the geoscientist Menard (in Science: growth and change. Harvard University Press, Cambridge, 1971): (1) that the peak year of citations for Hess’ article will be 1968; and (2) that the rate of citations to the article will then reach some lower level, continuing on accumulating citations at some regular but unimpressive rate. Drawing on data covering the period from 1962 to 2019, I show that Menard was close with respect to the first prediction. But I also show that things are less clear with respect to the second prediction. I explain why the data are less clear with respect to the second prediction.

AbstractWe present our approach for the identification of cited text spans in scientific literature, using pre-trained encoders (BERT) in combination with different neural networks. We further experiment to assess the impact of using these cited text spans as input in BERT-based extractive summarisation methods. Inspired and motivated by the CL-SciSumm shared tasks, we explore different methods to adapt pre-trained models which are tuned for generic domain to scientific literature. For the identification of cited text spans, we assess the impact of different configurations in terms of learning from augmented data and using different features and network architectures (BERT, XLNET, CNN, and BiMPM) for training. We show that identifying and fine-tuning the language models on unlabelled or augmented domain specific data can improve the performance of cited text span identification models. For the scientific summarisation we implement an extractive summarisation model adapted from BERT. With respect to the input sentences taken from the cited paper, we explore two different scenarios: (1) consider all the sentences (full-text) of the referenced article as input and (2) consider only the text spans that have been identified to be cited by other publications. We observe that in certain experiments, by using only the cited text-spans we can achieve better performance, while minimising the input size needed.