We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k). It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese). The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks. Using MTG, we train and analyze several popular multilingual generation models from different aspects. Our benchmark suite fosters model performance enhancement with more human-annotated parallel data. It provides comprehensive evaluations with diverse generation scenarios. Code and data are available at https://github.com/zide05/ MTG.

Automatic text summarization aims to produce a brief but crucial summary for the input documents. Both extractive and abstractive methods have witnessed great success in English datasets in recent years. However, there has been a minimal exploration of text summarization in Chinese, limited by the lack of largescale datasets. In this paper, we present a large-scale Chinese news summarization dataset CNewSum, which consists of 304,307 documents and human-written summaries for the news feed. It has long documents with high-abstractive summaries, which can encourage document-level understanding and generation for current summarization models. An additional distinguishing feature of CNewSum is that its test set contains adequacy and deducibility annotations for the summaries. The adequacy level measures the degree of summary information covered by the document, and the deducibility indicates the reasoning ability the model needs to generate the summary. These annotations can help researchers analyze and target their model performance bottleneck. We examine recent methods on CNewSum and release our dataset 1 to provide a solid testbed for automatic Chinese summarization research.

Abstract-An English idiom is a special combination of words, the meaning of which is not the simple addition of its constituents but a special meaning. English idioms are widely used by English native speakers, which are very important for English as a second language learners. Chinese learners are poor in English idioms comprehension, and they need to adopt a new method for improving their English idioms comprehension. S-S-P-C model means analyzing an English idiom from syntactic, semantic, pragmatic, and cultural perspective, which was applied in a teaching experiment among college students for one semester. The teaching experiment shows that S-S-P-C model is an efficient method for Chinese learners to help improve their English idioms comprehension.

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We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first and largest text generation benchmark with 120k human-annotated multi-way parallel data for three tasks (story generation, question generation, and title generation) across four languages (English, German, French, and Spanish). Based on it, we set various evaluation scenarios and make a deep analysis of several popular multilingual generation models from different aspects. Our benchmark suite will encourage the multilingualism for text generation community with more human-annotated parallel data and more diverse generation scenarios.