Dr. Markus Zopf

Research Interest

Artificial Intelligence
Machine Learning
Natural Language Processing
Automatic Text Summarization
Data-efficient Deep Learning

Publications

2019

Markus Zopf. 2019. Towards Context-free Information Importance Estimation. Ph. D. Thesis.

2018

Markus Zopf, Teresa Botschen, Tobias Falke, Benjamin Heinzerling, Ana Marasovic, Todor Mihaylov, Avinesh P.V.S., Eneldo Loza Mencía, Johannes Fürnkranz and Anette Frank (2018). What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization. In Proceedings of the Fifth International Conference on Social Networks Analysis, Management and Security, pages 272-277.

Markus Zopf, Eneldo Loza Mencía and Johannes Fürnkranz (2018). Which Scores to Predict in Sentence Regression for Text Summarization? In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1782–1791.

Markus Zopf (2018). Estimating Summary Quality with Pairwise Preferences. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1687–1696.

Markus Zopf (2018). auto-hMDS: Automatic Construction of a Large Heterogeneous Multi-Document Summarization Corpus. In Proceedings of the 11th International Conference on Language Resources and Evaluation, pages 3228-3233.

2016

Markus Zopf, Eneldo Loza Mencía and Johannes Fürnkranz (2016). Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization. In Proceedings of the 26th International Conference on Computational Linguistics, pages 1071-1082.

Markus Zopf, Maxime Peyrard and Judith Eckle-Kohler (2016). The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach. In Proceedings of the 26th International Conference on Computational Linguistics, pages 1535-1545.

Markus Zopf, Eneldo Loza Mencía and Johannes Fürnkranz (2016). Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, pages 84-94.

2015

Markus Zopf (2015). SeqCluSum: Combining Sequential Clustering and Contextual Importance Measuring to Summarize Developing Events over Time. In Proceedings of the 24th Text Retrieval Conference.

2012

Frederik Janssen and Markus Zopf (2012). The SeCo-Framework for Rule Learning. In Proceedings of the Workshop on Knowledge Discovery, Data Mining and Machine Learning, Lernen, Wissen, Adaptivität.