The task of automatic document summarization aims at generating short summaries for
originally long documents. A good summary should cover the most important information of …

The prevalent approach to sequence to sequence learning maps an input sequence to a
variable length output sequence via recurrent neural networks. We introduce an architecture …

Unlike extractive summarization, abstractive summarization has to fuse different parts of the
source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of …

Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and
land cover classification (LULC) approaches, however, are designed for cloud-free and …

We propose minimum risk training for end-to-end neural machine translation. Unlike
conventional maximum likelihood estimation, minimum risk training is capable of optimizing …

We propose a selective encoding model to extend the sequence-to-sequence framework for
abstractive sentence summarization. It consists of a sentence encoder, a selective gate …

Most previous seq2seq summarization systems purely depend on the source text to
generate summaries, which tends to work unstably. Inspired by the traditional template …

Building explainable systems is a critical problem in the field of Natural Language
Processing (NLP), since most machine learning models provide no explanations for the …

Neural encoder-decoder models have shown great success in many sequence generation
tasks. However, previous work has not investigated situations in which we would like to …

Coherence plays a critical role in producing a high-quality summary from a document. In
recent years, neural extractive summarization is becoming increasingly attractive. However …