Automatic text summarization: What has been done and what has to be done
This is an incremental review article that outlines challenges in automatic summarization for academics and developers, focusing on language-related issues.
The paper reviews the field of automatic text summarization, highlighting its long history since the 1950s and discussing recent works, problems, and limits, particularly those related to language nature, without presenting new experimental results or specific numbers.
Summaries are important when it comes to process huge amounts of information. Their most important benefit is saving time, which we do not have much nowadays. Therefore, a summary must be short, representative and readable. Generating summaries automatically can be beneficial for humans, since it can save time and help selecting relevant documents. Automatic summarization and, in particular, Automatic text summarization (ATS) is not a new research field; It was known since the 50s. Since then, researchers have been active to find the perfect summarization method. In this article, we will discuss different works in automatic summarization, especially the recent ones. We will present some problems and limits which prevent works to move forward. Most of these challenges are much more related to the nature of processed languages. These challenges are interesting for academics and developers, as a path to follow in this field.