Text summarization automatically produces a summary containing important sentences and
includes all relevant important information from the original document. One of the main …

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 …

Text Summarization is the task of extracting salient information from the original text
document. In this process, the extracted information is generated as a condensed report and …

Emerging events, such as the COVID pandemic and the Ukraine Crisis, require a time-
sensitive comprehensive understanding of the situation to allow for appropriate decision …

In recent times, there is an enormous amount of data available on the internet. It is laborious
for users to encapsulate large amount of data manually. Automatic text summarization can …

Generated topic label, an alternative representation of topics learned by topic model, is
widely used to help the user interpret the topics more efficiently. A major challenge now is to …

Creating a coherent summary of the text is a challenging task in the field of Natural
Language Processing (NLP). Various Automatic Text Summarization techniques have been …

The main idea of the automatic extractive text or opinion summarization is to find most
important representative small subset of the original document without any loss of important …

The information overload faced by today's society has created a big challenge for people
who want to look for relevant information from the internet. There are a lot of online …

We propose an abstractive summarization model using genetic semantic graph for
Indonesian news articles. The model is adapted from an abstractive summarization model …