In the past few years, neural abstractive text summarization with sequence-to-sequence
(seq2seq) models have gained a lot of popularity. Many interesting techniques have been …

Automatic text summarization has been studied in a variety of domains and languages.
However, this does not hold for the Russian language. To overcome this issue, we present …

The product reviews summarization task aims to automatically produce a short summary for
a set of reviews of a given product. Such summaries are expected to aggregate a range of …

With the emergence of deep learning, the attention of researchers has increased
significantly towards abstractive text summarization approaches. Though extractive text …

The development of large and super-large language models, such as GPT-3, T5, Switch
Transformer, ERNIE, etc., has significantly improved the performance of text generation. One …

Due to the massive volume of textual data which is produced by the Internet and in other
archives of like news stories, academic papers, legal documents, etc., automatic text …

The existing dialogue summarization corpora are significantly extractive. We introduce a
methodology for dataset extractiveness evaluation and present a new low-extractive corpus …

[Objective] This paper tries to summarize interactive contents from new media for
government affairs based on topic clustering, aiming to help the government effectively …

Encoder–decoder models have achieved high performance in their application to keyphrase
generation. However, keyphrases for a source text generated by these models are similar to …

Neural text generation has been a challenging task, among which the text representation
and the beam search are crucial techniques. By improving these techniques, we propose a …