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 …

In the period since 2004, many novel sophisticated approaches for generic multi-document
summarization have been developed. Intuitive simple approaches have also been shown to …

With the evolution of the Internet and multimedia technology, the amount of text data has
increased exponentially. This text volume is a precious source of information and knowledge …

As information is available in abundance for every topic on internet, condensing the
important information in the form of summary would benefit a number of users. Hence, there …

Generating a text abstract from a set of documents remains a challenging task. The neural
encoder-decoder framework has recently been exploited to summarize single documents …

The increase in online text generation by humans and machines needs automatic text
summarization systems. Recent research studies commonly use deep learning, besides …

Textual information in the form of digital documents quickly accumulates to create huge
amounts of data. The majority of these documents are unstructured: it is unrestricted text and …

We propose a new, ambitious framework for abstractive summarization, which aims at
selecting the content of a summary not from sentences, but from an abstract representation …

Multi-document summarization involves many aspects of content selection and surface
realization. The summaries must be informative, succinct, grammatical, and obey stylistic …

In concept-based summarization, sentence selection is modelled as a budgeted maximum
coverage problem. As this problem is NP-hard, pruning low-weight concepts is required for …