Guiding Theme C2: Methods for contextual and constraint-based ranking

Goal of this Guiding theme is the development of suitable ranking algorithms that will be used in other parts of the project, with a particular focus on multi-document summarization. Possible thesis topics relate to ranking problems in multi-document summarization.

The first key task is to support multi-document summarization by ranking sentences which can be selected for a summary (cf. B2). A key challenge is to find a suitable calibration point that separates relevant from not relevant sentences and thus allows a termination of the selection process when all relevant sentences have been added. For this, we intend to adapt methods that have been developed for label ranking (Hüllermeier & Fürnkranz, 2010) to object ranking problems. Also, rankings will be computed dynamically. When a sentence is selected for inclusion into the summary, this may change the ranking of other sentences because some of them may no longer be necessary because the information they carry has already been included.

The second key problem is to develop techniques that are able to combine multiple local rankings to an overall ranking which respects a given set of global constraints. Ranking problems occur at different levels of a language processing chain, such as the semantic level, where multiple meanings of a word must be ranked, or the discourse level, where common entities and complex event structures have to be identified across heterogeneous documents (cf. A1 and A2). These rankings problems can be solved in isolation, but they also have to respect global constraints.

Poster (in German)

Example thesis topics

  • Bipartite Ranking for iterative Multi-Document Summarization
  • Algorithms for the Prediction of Partial Orders
  • A Framework for constraint-based Re-ranking in NLP


  1. Fürnkranz, J. und Hüllermeier, E., editors (2011). Preference Learning. Springer-Verlag.
  2. Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., und Brinker, K. (2008). Multilabel Classification via Calibrated Label Ranking. Machine Learning, 73(2):133–153.
  3. Hüllermeier, E. und Fürnkranz, J. (2010). On Predictive Accuracy and Risk Minimization in Pairwise Label Ranking. Journal of Computer and System Sciences, 76(1):49–62.
  4. Hüllermeier, E., Fürnkranz, J., Cheng, W., und Brinker K. (2008). Label Ranking by Learning Pairwise Preferences. Artificial Intelligence, 172(16-17):1897–1916.
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