CERMINE is a comprehensive open-source system for extracting structured metadata from scientific articles in a born-digital form. The system is based on a modular workflow, whose loosely coupled architecture allows for individual component evaluation and adjustment, enables effortless improvements and replacements of independent parts of the algorithm and facilitates future architecture expanding. The implementations of most steps are based on supervised and unsupervised machine learning techniques, which simplifies the procedure of adapting the system to new document layouts and styles. The evaluation of the extraction workflow carried out with the use of a large dataset showed good performance for most metadata types, with the average F score of 77.5 %. CERMINE system is available under an open-source licence and can be accessed at http://cermine.ceon.pl. In this paper, we outline the overall workflow architecture and provide details about individual steps implementations. We also thoroughly compare CERMINE to similar solutions, describe evaluation methodology and finally report its results.

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CERMINE: automatic extraction of structured metadata from scientific literature

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of groundbreaking applications in chemistry, neurology, electronics, or communication networks, among others. At the current stage of research, however, the efficient processing of GNNs is still an open challenge for several reasons. Besides of their novelty, GNNs are hard to compute due to their dependence on the input graph, their combination of dense and very sparse operations, or the need to scale to huge graphs in some applications. In this context, this paper aims to make two main contributions. On the one hand, a review of the field of GNNs is presented from the perspective of computing. This includes a brief tutorial on the GNN fundamentals, an overview of the evolution of the field in the last decade, and a summary of operations carried out in the multiple phases of different GNN algorithm variants. On the other hand, an in-depth analysis of current software and hardware acceleration schemes is provided, from which a hardware-software, graph-aware, and communication-centric vision for GNN accelerators is distilled.

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality labeled datasets with both visual and textual information are still insufficient. In this paper, we present \textbf{DocBank}, a benchmark dataset that contains 500K document pages with fine-grained token-level annotations for document layout analysis. DocBank is constructed using a simple yet effective way with weak supervision from the \LaTeX{} documents available on the this http URL. With DocBank, models from different modalities can be compared fairly and multi-modal approaches will be further investigated and boost the performance of document layout analysis. We build several strong baselines and manually split train/dev/test sets for evaluation. Experiment results show that models trained on DocBank accurately recognize the layout information for a variety of documents. The DocBank dataset is publicly available at \url{this https URL}.

DocBank: A Benchmark Dataset for Document Layout Analysis

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naive-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.

Information extraction from scientific articles: a survey

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction to automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles.

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Citation recommendation: approaches and datasets

CERMINE is a comprehensive open source system for extracting metadata and parsed bibliographic references from scientific articles in born-digital form. The system is based on a modular workflow, whose architecture allows for single step training and evaluation, enables effortless modifications and replacements of individual components and simplifies further architecture expanding. The implementations of most steps are based on supervised and unsupervised machine-learning techniques, which simplifies the process of adjusting the system to new document layouts. The paper describes the overall workflow architecture, provides details about individual implementations and reports evaluation methodology and results. CERMINE service is available at http://cermine.ceon.pl.

CERMINE -- Automatic Extraction of Metadata and References from Scientific Literature

Scientific literature analysis improves knowledge propagation and plays a key role in understanding and assessment of scholarly communication in scientific world. In recent years many tools and services for analysing the content of scientific articles have been developed. One of the most important tasks in this research area is understanding the roles of different parts of the document. It is impossible to build effective solutions for problems related to document fragments classification and evaluate their performance without a reliable test set, that contains both input documents and the expected results of classification. In this paper we present GROTOAP2 � a large dataset of ground truth files containing labelled fragments of scientific articles in PDF format, useful for training and evaluation of document content analysis-related solutions. GROTOAP2 was successfully used for training CERMINE � our system for extracting metadata and content from scientific articles. The dataset is based on articles from PubMed Central Open Access Subset. GROTOAP2 is published under Open Access license. The semi-automatic method used to construct GROTOAP2 is scalable and can be adjusted for building large datasets from other data sources. The article presents the content of GROTOAP2, describes the entire creation process and reports the evaluation methodology and results.

Pawel Szostek

Papers

CERMINE: automatic extraction of structured metadata from scientific literature

CERMINE -- Automatic Extraction of Metadata and References from Scientific Literature

GROTOAP2 The Methodology of Creating a Large Ground Truth Dataset of Scientific Articles

CeON/CERMINE: CERMINE 1.13