Julius Steen


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Abstractive Timeline Summarization
Julius Steen | Katja Markert
Proceedings of the 2nd Workshop on New Frontiers in Summarization

Timeline summarization (TLS) automatically identifies key dates of major events and provides short descriptions of what happened on these dates. Previous approaches to TLS have focused on extractive methods. In contrast, we suggest an abstractive timeline summarization system. Our system is entirely unsupervised, which makes it especially suited to TLS where there are very few gold summaries available for training of supervised systems. In addition, we present the first abstractive oracle experiments for TLS. Our system outperforms extractive competitors in terms of ROUGE when the number of input documents is high and the output requires strong compression. In these cases, our oracle experiments confirm that our approach also has a higher upper bound for ROUGE scores than extractive methods. A study with human judges shows that our abstractive system also produces output that is easy to read and understand.


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Universal Dependencies are Hard to Parse – or are They?
Ines Rehbein | Julius Steen | Bich-Ngoc Do | Anette Frank
Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)


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Detecting Annotation Scheme Variation in Out-of-Domain Treebanks
Yannick Versley | Julius Steen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

To ensure portability of NLP systems across multiple domains, existing treebanks are often extended by adding trees from interesting domains that were not part of the initial annotation effort. In this paper, we will argue that it is both useful from an application viewpoint and enlightening from a linguistic viewpoint to detect and reduce divergence in annotation schemes between extant and new parts in a set of treebanks that is to be used in evaluation experiments. The results of our correction and harmonization efforts will be made available to the public as a test suite for the evaluation of constituent parsing.