Summarization corpora are numerous but fragmented, making it challenging for researchers to efficiently pinpoint corpora most suited to a given summarization task. In this paper, we introduce a repository containing corpora available to train and evaluate automatic summarization systems. We also present an overview of the main corpora with respect to the different summarization tasks, and identify various corpus parameters that researchers may want to consider when choosing a corpus. Lastly, as the recent successes of artificial neural networks for summarization have renewed the interest in creating large-scale corpora for summarization, we survey which corpora are used in neural network research studies. We come to the conclusion that more large-scale corpora for summarization are needed. Furthermore, each corpus is organized differently, which makes it time-consuming for researchers to experiment a new summarization algorithm on many corpora, and as a result studies typically use one or very few corpora. Agreeing on a data standard for summarization corpora would be beneficial to the field.
@InProceedings{DERNONCOURT18.889, author = {Franck Dernoncourt and Mohammad Ghassemi and Walter Chang}, title = "{A Repository of Corpora for Summarization}", 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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