International Journal on Digital Libraries最新文献

In the shifting landscape of sharing knowledge, it is no longer only about writing papers. After a paper is written, what comes next is an integral part of the process. This special issue delves into the transformative landscape of scholarly communication, exploring novel methodologies and technologies reshaping how scholarly content is generated, recommended, explored and analysed. Indeed, the contemporary perspective on scholarly publication recognizes the centrality of post-publication activities. The criticality of refining and scrutinizing manuscripts has gained prominence, surpassing the act of dissemination. The emphasis has shifted from publication to ensuring visibility and comprehension of the conveyed content. The papers compiled in this special issue scrutinize these evolving dynamics. They delve into the intricacies of post-processing and close examination of manuscripts, acknowledging the impact of these aspects. The overarching objective is to stimulate scholarly discussions on the evolving nature of communication in academia.

In this paper, we compare the performance of several popular pre-trained reference extraction and segmentation toolkits combined in different pipeline configurations on three different datasets. The extraction is end-to-end, i.e. the input is PDF documents, and the output is parsed reference objects. The evaluation is for reference strings and individual fields in the reference objects using alignment by identical fields and close-to-identical values. Our results show that Grobid and AnyStyle perform best of all compared tools, although one may want to use them in combination. Our work is meant to serve as a reference for researchers interested in applying out-of-the-box reference extraction and -parsing tools, for example, as a preprocessing step to a more complex research question. Our detailed results on different datasets with results for individual parsed fields will allow them to focus on aspects that are particularly important to them.

We propose a method for visualizing literary works that quantitatively extracts the mutual relationships among play characters from the narrative of a storyline. The method first determines the cross-correlation of the appearance frequencies in the time domain between two play characters, which is calculated for all pairs of characters in each narrative. We also calculate the correlation among three play characters to find unique triangular relationships. Then we create a graphical representation of the relationships using node-link representations based on a physical potential model. The method is suitable for dramas, as demonstrated for ten famous Shakespeare plays. The resulting visualizations show good agreement with the conventional understanding of each play and also provide new insight into Shakespearean criticism.

E-reading has become more popular by making the number of book readers high in number. With online book reading websites, it is much simpler to read any book at any time by simply typing its name into a search engine. These websites offer free reading platform to users with unlimited number of choices without exceeding any rights. However, statistics reveal that reading is dwindling, particularly among young people. In this survey, we presented several existing approaches employed to design a book recommendation system from 2012 to 2023. Different types of datasets, used to extract information about books and users, in terms of features, source and usage were discussed. Six different categories for book recommendation techniques have been recognized and discussed which would build the groundwork for future study in this area. The issues related to book recommendation system was also briefly discussed. We have discussed on the performance analysis of various research works on book recommendation system. We have also highlighted the research concerns and future scope to improve the performance of book recommender system. We hope these findings will help researchers to explore more in book recommender systems particularly.

An abstract is the most crucial element that may convince readers to read the complete text of a scientific publication. However, studies show that in terms of organization, readability, and style, abstracts are also among the most troublesome parts of the pertinent manuscript. The ultimate goal of this article is to produce better understandable abstracts with automatic methods that will contribute to scientific communication in Turkish. We propose a summarization system based on extractive techniques combining general features that have been shown to be beneficial for Turkish. To construct the data set for this aim, a sample of 421 peer-reviewed Turkish articles in the field of librarianship and information science was developed. First, the structure of the full-texts, and their readability in comparison with author abstracts, were examined for text quality evaluation. A content-based evaluation of the system outputs was then carried out. System outputs, in cases of using and ignoring structural features of full-texts, were compared. Structured outputs outperformed classical outputs in terms of content and text quality. Each output group has better readability levels than their original abstracts. Additionally, it was discovered that higher-quality outputs are correlated with more structured full-texts, highlighting the importance of structural writing. Finally, it was determined that our system can facilitate the scholarly communication process as an auxiliary tool for authors and editors. Findings also indicate the significance of structural writing for better scholarly communication.

Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and extract key elements of ETD documents. We explain how we construct the datasets by manual labeling the data or by deriving labeled data through synthetic processes. We demonstrate how our datasets can be used to develop downstream applications and to evaluate, retrain, or fine-tune pre-trained machine learning models. We describe our ongoing work to compile benchmark datasets and exploit machine learning techniques to build intelligent digital libraries for ETDs.