Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Contemporary works on abstractive text summarization have focused primarily on high-
resource languages like English, mostly due to the limited availability of datasets for low/mid …

We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of
crosslingual abstractive summarization systems. We extract article and summary pairs in 18 …

Cross-lingual summarization is the task of generating a summary in one language (eg,
English) for the given document (s) in a different language (eg, Chinese). Under the …

We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained
from online newspapers, it contains 1.5 M+ article/summary pairs in five different languages …

The textual similarity is a crucial aspect for many extractive text summarization methods. A
bag-of-words representation does not allow to grasp the semantic relationships between …

Many applications within natural language processing involve performing text-to-text
transformations, ie, given a text in natural language as input, systems are required to …

Recent advancements in high-quality, large-scale English resources have pushed the
frontier of English Automatic Text Simplification (ATS) research. However, less work has …

To adapt text summarization to the multilingual world, previous work proposes multi-lingual
summarization (MLS) and cross-lingual summarization (CLS). However, these two tasks …

Multimodal abstractive summarization (MAS) aims to produce a concise summary given the
multimodal data (text and vision). Existing studies mainly focus on how to effectively use the …