Summarization, is to reduce the size of the document while preserving the meaning, is one
of the most researched areas among the Natural Language Processing (NLP) community …

Text summarization is the process of condensing a long text into a shorter version by
maintaining the key information and its meaning. Automatic text summarization can save …

Large language models (LLMs) have shown promise for automatic summarization but the
reasons behind their successes are poorly understood. By conducting a human evaluation …

A survey of computational methods for understanding, generating, and manipulating human
language, which offers a synthesis of classical representations and algorithms with …

In this paper, we develop a neural summarization model which can effectively process
multiple input documents and distill Transformer architecture with the ability to encode …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

We propose a new framework for abstractive text summarization based on a sequence-to-
sequence oriented encoder-decoder model equipped with a deep recurrent generative …

It has now been 50 years since the publication of Luhn's seminal paper on automatic
summarization. During these years the practical need for automatic summarization has …

This article presents new alternatives to the similarity function for the TextRank algorithm for
automatic summarization of texts. We describe the generalities of the algorithm and the …

Graphs that capture relations between textual units have great benefits for detecting salient
information from multiple documents and generating overall coherent summaries. In this …