This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint.It introduces a novel method that encourages the inclusion of key terms from the original document into the summary: key terms are masked out of the original document and must be filled in by a coverage model using the current generated summary.A novel unsupervised training procedure leverages this coverage model along with a fluency model to generate and score summaries.When tested on popular news summarization datasets, the method outperforms previous unsupervised methods by more than 2 R-1 points, and approaches results of competitive supervised methods.Our model attains higher levels of abstraction with copied passages roughly two times shorter than prior work, and learns to compress and merge sentences without supervision.

Cross-lingual text classification (CLTC) refers to the task of classifying documents in different languages into the same taxonomy of categories. An open challenge in CLTC is to classify documents for the languages where labeled training data are not available. Existing approaches rely on the availability of either high-quality machine translation of documents (to the languages where massively training data are available), or rich bilingual dictionaries for effective translation of trained classification models (to the languages where labeled training data are lacking). This paper studies the CLTC challenge under the assumption that neither condition is met. That is, we focus on the problem of translating classification models with highly incomplete bilingual dictionaries. Specifically, we propose two new approaches that combines unsupervised word embedding in different languages, supervised mapping of embedded words across languages, and probabilistic translation of classification models. The approaches show significant performance improvement in CLTC on a benchmark corpus of Reuters news stories (RCV1/RCV2) in English, Spanish, German, French and Chinese and an internal dataset in Uzbek, compared to representative baseline methods using conventional bilingual dictionaries or highly incomplete ones.

Event extraction has become one of the most important topics in information extraction, but to date, there is very limited work on leveraging cross-lingual training to boost performance. We propose a new event extraction approach that trains on multiple languages using a combination of both language-dependent and language-independent features, with particular focus on the case where target domain training data is of very limited size. We show empirically that multilingual training can boost performance for the tasks of event trigger extraction and event argument extraction on the Chinese ACE 2005 dataset.