34 citations · 90 across the 10 of their papers we have counts for
6 papers · 1 filter
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
Qian Ruan, Malte Ostendorff, Georg Rehm
Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified…
Evaluating Document Representations for Content-based Legal Literature Recommendations
Malte Ostendorff, Elliott Ash, Terry Ruas +3
Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflec…
Aspect-based Document Similarity for Research Papers
Malte Ostendorff, Terry Ruas, Till Blume +2
Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two document…
Towards Discourse Parsing-inspired Semantic Storytelling
Georg Rehm, Karolina Zaczynska, Julián Moreno-Schneider +6
Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term visi…
Named Entities in Medical Case Reports: Corpus and Experiments
Sarah Schulz, Jurica Ševa, Samuel Rodriguez +2
We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central's open access library. In the case reports, we annotate cases, c…
Enriching BERT with Knowledge Graph Embeddings for Document Classification
Malte Ostendorff, Peter Bourgonje, Maria Berger +3
In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we de…