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he advancement of web programming techniques, such as Ajax and jQuery, and datastores, such as Apache Solr and Elasticsearch, have made it much easier to deploy small to medium scale web- based search engines. However, developing a sustainable search engine that supports scholarly big data services is still challenging often because of limited human resources and financial support. Such scenarios are typical in academic settings or small businesses. Here, we showcase how four key design decisions were made by trading-off competing factors such as performance, cost, and effi- ciency, when developing the Next Generation CiteSeerX (NGX), the successor of CiteSeerX, which was a pioneering digital library search engine that has been serving academic communities for more than two decades. This work extends our previous work in Wu et al. (2021) and discusses design considerations of infrastruc- ture, web applications, indexing, and document filtering. These design considerations can be generalized to other web-based search engines with a similar scale that are deployed in small business or academic settings with limited resources. 

Recently, the Allen Institute for Artificial Intelligence released the Semantic Scholar Open Research Corpus (S2ORC), one of the largest open-access scholarly big datasets with more than 130 million schol- arly paper records. S2ORC contains a significant portion of automat- ically generated metadata. The metadata quality could impact down- stream tasks such as citation analysis, citation prediction, and link analysis. In this project, we assess the document linking quality and estimate the document conflation rate for the S2ORC dataset. Using semi-automatically curated ground truth corpora, we estimated that the overall document linking quality is high, with 92.6% of documents correctly linking to six major databases, but the linking quality varies depending on subject domains. The document confla- tion rate is around 2.6%, meaning that about 97.4% of documents are unique. We further quantitatively compared three near-duplicate detection methods using the ground truth created from S2ORC. The experiments indicated that locality-sensitive hashing was the best method in terms of effectiveness and scalability, achieving high performance (F1=0.960) and a much reduced runtime. Our code and data are available at https://github.com/lamps-lab/docconflation. 

Presentation slides describing the content of scientific and technical papers are an efficient and effective way to present that work. However, manually generating presentation slides is labor intensive. We propose a method to automatically generate slides for scientific papers based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score. 

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using nine million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro- F 1 measure of 0.76 with F 1 of individual subject categories ranging from 0.50 to 0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers. 

The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources. Crawl frontiers thus need to be optimized to improve the coverage and freshness of crawled content. In this paper, we propose an approach for modeling the dynamics of change in the web using archived copies of webpages. To evaluate its utility, we conduct a preliminary study on the scholarly web using 19,977 seed URLs of authors’ homepages obtained from their Google Scholar profiles. We first obtain archived copies of these webpages from the Internet Archive (IA), and estimate when their actual updates occurred. Next, we apply maximum likelihood to estimate their mean update frequency ( ) values. Our evaluation shows that   values derived from a short history of archived data provide a good estimate for the true update frequency in the short-term, and that our method provides better estimations of updates at a fraction of resources compared to the baseline models. Based on this, we demonstrate the utility of archived data to optimize the crawling strategy of web crawlers, and uncover important challenges that inspire future research directions. 

Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, ACKEXTRACT based on open-source text mining software and evaluate our method using manually labeled data. ACKEXTRACT uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F1 = 0:92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by ACKEXTRACT including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged. We further analyze chronological trends of acknowledgement entities in CORD-19 papers. All codes and labeled data are publicly available at https://github.com/ lamps-lab/ackextract. 

Scholarly digital libraries provide access to scientific publications and comprise useful resources for researchers who search for literature on specific subject areas. CiteSeerX is an example of such a digital library search engine that provides access to more than 10 million academic documents and has nearly one million users and three million hits per day. Artificial Intelligence (AI) technologies are used in many components of CiteSeerX including Web crawling, document ingestion, and metadata extraction. CiteSeerX also uses an unsupervised algorithm called noun phrase chunking (NP-Chunking) to extract keyphrases out of documents. However, often NP-Chunking extracts many unimportant noun phrases. In this paper, we investigate and contrast three supervised keyphrase extraction models to explore their deployment in CiteSeerX for extracting high quality keyphrases. To perform user evaluations on the keyphrases predicted by different models, we integrate a voting interface into CiteSeerX. We show the development and deployment of the keyphrase extraction models and the maintenance requirements.