Abstractive text summarization has recently improved its performance due to the use of sequence to sequence models. However, while these models are extremely data-hungry, datasets in languages other than English are few. In this work, we introduce WITS (Wikipedia for Italian Text Summarization), a largescale dataset built exploiting Wikipedia articles’ structure. WITS contains almost 700,000 Wikipedia articles, together with their human-written summaries. Compared to existing data for text summarization in Italian, WITS is more than an order of magnitude larger and more challenging given its lengthy sources. We explore WITS characteristics and present some baselines for future work.
WITS: Wikipedia for Italian Text Summarization
Casola Silvia;Lavelli Alberto
2021-01-01
Abstract
Abstractive text summarization has recently improved its performance due to the use of sequence to sequence models. However, while these models are extremely data-hungry, datasets in languages other than English are few. In this work, we introduce WITS (Wikipedia for Italian Text Summarization), a largescale dataset built exploiting Wikipedia articles’ structure. WITS contains almost 700,000 Wikipedia articles, together with their human-written summaries. Compared to existing data for text summarization in Italian, WITS is more than an order of magnitude larger and more challenging given its lengthy sources. We explore WITS characteristics and present some baselines for future work.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
