Automatic summarization has so far focused on datasets of ten to twenty rather short documents, typically news articles. But automatic systems could in theory analyze hundreds of documents from a wide range of sources and provide an overview to the interested reader. Such a summary would ideally present the most general issues of a given topic and allow for more in-depth information on specific aspects within said topic. In this paper, we present a new approach for creating hierarchical summarization corpora from large, heterogeneous document collections. We first extract relevant content using crowdsourcing and then ask trained annotators to order the relevant information hierarchically. This yields tree structures covering the specific facets discussed in a document collection. Our resulting corpus is freely available and can be used to develop and evaluate hierarchical summarization systems.
@InProceedings{TAUCHMANN18.252, author = {Christopher Tauchmann and Thomas Arnold and Andreas Hanselowski and Christian M. Meyer and Margot Mieskes}, title = "{Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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