SummaryRank

SummaryRank is a python package dedicated to supporting ranking experiments over sentence/summary data. It has implemented a range of basic functions, such as data imports, representations/features generation, and feature vectors split/join operations (for SVMLight format), to make ranking experiments easy.

As of January 2016, this package supports the following sets of features:

Query-biased summarization features from Metzler and Kanungo (2008)
ESA and Word2Vec features from Chen et al. (2015)
TAGME feature and context meta-features from Yang et al. (2016)

If you use this package in your research work, please cite the following paper:

Liu Yang, Qingyao Ai, Damiano Spina, Ruey-Cheng Chen, Liang Pang, W. Bruce Croft, 
Jiafeng Guo and Falk Scholer.  Beyond factoid QA: Effective methods for non-factoid 
answer sentence retrieval. In Proceedings of ECIR '16, to appear. 2016.

Detailed installation instructions and usage will be made available shortly.

Contributors

Ruey-Cheng Chen
Damiano Spina
Liu Yang
Qingyao Ai

References

Ruey-Cheng Chen, Damiano Spina, W. Bruce Croft, Mark Sanderson, and Falk Scholer. Harnessing semantics for answer sentence retrieva. In Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '15 (CIKM workshop), pages 21–27. ACM, 2015.

Donald Metzler and Tapas Kanungo. Machine Learned Sentence Selection Strategies for Query-Biased Summarization. In Proceedings of SIGIR 2008 Learning to Rank Workshop, pages 40–47. ACM, 2008.

Liu Yang, Qingyao Ai, Damiano Spina, Ruey-Cheng Chen, Liang Pang, W. Bruce Croft, Jiafeng Guo and Falk Scholer. Beyond factoid QA: Effective methods for non-factoid answer sentence retrieval. In Proceedings of ECIR '16, to appear. 2016.