The document-centric workflows in science have reached (or already exceeded) the limits of
adequacy. This is emphasized by recent discussions on the increasing proliferation of …

The automatic semantic structuring of scientific text allows for more efficient reading of
research articles and is an important indexing step for academic search engines. Sequential …

We examine the novel task of domain-independent scientific concept extraction from
abstracts of scholarly articles and present two contributions. First, we suggest a set of …

Motivation Scientists increasingly rely on intelligent information systems to help them in their
daily tasks, in particular for managing research objects, like publications or datasets. The …

We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for
Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1. 0) …

Abstract In Natural Language Processing (NLP) domain, the majority of automatic text
summarization approaches depend on a prior knowledge of the language and/or the …

Background The biodiversity domain, and in particular biological taxonomy, is moving in the
direction of semantization of its research outputs. The present work introduces OpenBiodiv …

Current science communication has a number of drawbacks and bottlenecks which have
been subject of discussion lately: Among others, the rising number of published articles …

This paper surveys the current state of the art in document automation (DA). The objective of
DA is to reduce the manual effort during the generation of documents by automatically …

Purpose This paper introduces the Research Articles in Simplified HTML (or RASH), which
is a Web-first format for writing HTML-based scholarly papers; it is accompanied by the …