We introduce extreme summarization, a new single-document summarization task which
does not favor extractive strategies and calls for an abstractive modeling approach. The idea …

The enormous quantity of text makes it challenging for users to obtain the key information
and knowledge. Automatic text summarization can alleviate this problem by providing …

Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art
performance in terms of ROUGE scores, because they directly use ROUGE as the rewards …

In recent years, a large amount of data has been accumulated, such as those recorded in
geological journals and report literature, which contain a wealth of information, but these …

We present a new neural model for text summarization that first extracts sentences from a
document and then compresses them. The proposed model offers a balance that sidesteps …

We propose encoder-centric stepwise models for extractive summarization using structured
transformers--HiBERT and Extended Transformers. We enable stepwise summarization by …

There has been substantial progress in summarization research enabled by the availability
of novel, often large-scale, datasets and recent advances on neural network-based …

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that
comprises four elements: faithfulness (degree of factual consistency with the source), focus …

Extractive summaries are usually presented as lists of sentences with no expected cohesion
between them and with plenty of redundant information if not accounted for. In this paper, we …

This paper presents a new method for learning typed entailment graphs from text. We extract
predicate-argument structures from multiple-source news corpora, and compute local …