Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seq-keyphrase.

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Different texts shall by nature correspond to different number of keyphrases. This desideratum is largely missing from existing neural keyphrase generation models. In this study, we address this problem from both modeling and evaluation perspectives.We first propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences. Generation diversity is further enhanced with two novel techniques by manipulating decoder hidden states. In contrast to previous approaches, our model is capable of generating diverse keyphrases and controlling number of outputs.We further propose two evaluation metrics tailored towards the variable-number generation. We also introduce a new dataset (ST A C KEX) that expands beyond the only existing genre (i.e., academic writing) in keyphrase generation tasks. With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.

The increasing popularity of academic social networking sites (ASNSs) requires studies on the usage of ASNSs among scholars and evaluations of the effectiveness of these ASNSs. However, it is unclear whether current ASNSs have fulfilled their design goal, as scholars' actual online interactions on these platforms remain unexplored. To fill the gap, this article presents a study based on data collected from ResearchGate. Adopting a mixed‐method design by conducting qualitative content analysis and statistical analysis on 1,128 posts collected from ResearchGate Q&A, we examine how scholars exchange information and resources, and how their practices vary across three distinct disciplines: library and information services, history of art, and astrophysics. Our results show that the effect of a questioner's intention (i.e., seeking information or discussion) is greater than disciplinary factors in some circumstances. Across the three disciplines, responses to questions provide various resources, including experts' contact details, citations, links to Wikipedia, images, and so on. We further discuss several implications of the understanding of scholarly information exchange and the design of better academic social networking interfaces, which should stimulate scholarly interactions by minimizing confusion, improving the clarity of questions, and promoting scholarly content management.

Although there are a number of social networking services that specifically target scholars, little has been published about the actual practices and the usage of these so‐called academic social networking services (ASNSs). To fill this gap, we explore the populations of academics who engage in social activities using an ASNS; as an indicator of further engagement, we also determine their various motivations for joining a group in ASNSs. Using groups and their members in Mendeley as the platform for our case study, we obtained 146 participant responses from our online survey about users' common activities, usage habits, and motivations for joining groups. Our results show that (a) participants did not engage with social‐based features as frequently and actively as they engaged with research‐based features, and (b) users who joined more groups seemed to have a stronger motivation to increase their professional visibility and to contribute the research articles that they had read to the group reading list. Our results generate interesting insights into Mendeley's user populations, their activities, and their motivations relative to the social features of Mendeley. We also argue that further design of ASNSs is needed to take greater account of disciplinary differences in scholarly communication and to establish incentive mechanisms for encouraging user participation.

Over the last five years, a range of projects have focused on progressively more elaborated techniques for adaptive news delivery. However, the adaptation process in these systems has become more complicated and thus less transparent to the users. In this paper, we concentrate on the application of open user models in adding transparency and controllability to adaptive news systems. We present a personalized news system, YourNews, which allows users to view and edit their interest profiles, and report a user study on the system. Our results confirm that users prefer transparency and control in their systems, and generate more trust to such systems. However, similar to previous studies, our study demonstrate that this ability to edit user profiles may also harm the system's performance and has to be used with caution.

Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normalsimple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we propose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple stateof-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https://github.com/ Sanqiang/text_simplification.

The emergence of the iSchool movement and the establishment of iSchools have helped to reshape the landscape of the library and information science (LIS) discipline. In this article, based on a set of research questions focusing around the research and education efforts of about 25 iSchools, we performed a study using both quantitative and qualitative methods on publically available data obtained from the web. Our results show that iSchools share the same vision and mission of working on relationships between information, people and technology, and have established themselves as the appropriate institutions for researchers from diverse subject areas to study this interdisciplinary integration. Overall, we are seeing an emerging iSchool identity and a defining iField, but there are still many important developments to make.