Web-crawled corpora are essential resources for linguistic and NLP research, offering far more data than is available from curated corpora. However, they often contain a great deal of low-quality texts which can complicate research and degrade the quality of pre-trained language models. Therefore, they are typically filtered, e.g. by applying rules or classifiers. In this paper, we compare the effectiveness of various text filtering classifiers and measure their impact on language model performance for three medium-resource languages. We present TQ-IS, an Icelandic text quality dataset consisting of 2,000 web-crawled documents, in which spans of low-quality text have been manually identified and labeled. We then evaluate a perplexity-based classifier, a supervised classifier trained on TQ-IS, and a self-supervised classifier trained to discern between documents from curated and web-crawled corpora on Icelandic, Estonian and Basque. We find that these classifiers obtain F1 scores of 94.48%, 99.01% and 93.40%, respectively, when evaluated on the TQ-IS dataset. Furthermore, our results show that while adding filtered web-crawled text to a pre-training corpus can improve downstream performance for pre-trained language models, any improvement is likely to remain modest unless the web-crawled corpus is significantly larger in size.

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. We find that an encoder-decoder model with a sequence-to-sequence based extractor obtains the best results, outperforming all baseline methods. Furthermore, we evaluate how the size of the training corpus affects the quality of the generated summaries. We release the corpus and the models with an open license.

In this paper, we present a character-based BiLSTM model for splitting Icelandic compound words, and show how varying amounts of training data affects the performance of the model. Compounding is highly productive in Icelandic, and new compounds are constantly being created. This results in a large number of out-of-vocabulary (OOV) words, negatively impacting the performance of many NLP tools. Our model is trained on a dataset of 2.9 million unique word forms and their constituent structures from the Database of Icelandic Morphology. The model learns how to split compound words into two parts and can be used to derive the constituent structure of any word form. Knowing the constituent structure of a word form makes it possible to generate the optimal split for a given task, e.g., a full split for subword tokenization, or, in the case of part-of-speech tagging, splitting an OOV word until the largest known morphological head is found. The model outperforms other previously published methods when evaluated on a corpus of manually split word forms. This method has been integrated into Kvistur, an Icelandic compound word analyzer.

Compounding is extremely productive in Icelandic and multi-word compounds are common. The likelihood of finding previously unseen compounds in texts is thus very high, which makes out-of-vocabulary words a problem in the use of NLP tools. The tool de-scribed in this paper splits Icelandic compounds and shows their binary constituent structure. The probability of a constituent in an unknown (or unanalysed) compound forming a combined constituent with either of its neighbours is estimated, with the use of data on the constituent structure of over 240 thousand compounds from the Database of Modern Icelandic Inflection, and word frequencies from Íslenskur orðasjóður, a corpus of approx. 550 million words. Thus, the structure of an unknown compound is derived by com-parison with compounds with partially the same constituents and similar structure in the training data. The granularity of the split re-turned by the decompounder is important in tasks such as semantic analysis or machine translation, where a flat (non-structured) se-quence of constituents is insufficient.