IceSum: An Icelandic Text Summarization Corpus

Abstract

Automatic Text Summarization (ATS) is the task of generating concise and fluent summaries from one or more documents. In this paper, we present IceSum, the first Icelandic corpus annotated with human-generated summaries. IceSum consists of 1,000 online news articles and their extractive summaries. We train and evaluate several neural network-based models on this dataset, comparing them against a selection of baseline methods. The best model obtains a ROUGE-2 recall score of 71.06, outperforming all baseline methods. Furthermore, we evaluate how the amount of training data affects the quality of the generated summaries. Our results show that while the corpus is sufficiently large to train a well-performing model, there could still be significant gains from increasing the size of the training set. We release the corpus and the models with an open license.