Automatic Keyword Extraction for Text Summarization: A Survey

TL;DR

This survey reviews recent methods and challenges in automatic keyword extraction and text summarization, emphasizing their importance in managing rapidly growing textual data across various domains.

Contribution

It provides a comprehensive overview of current techniques, datasets, evaluation metrics, and future research directions in automatic keyword extraction and text summarization.

Findings

Different methodologies for keyword extraction and summarization are discussed.

Various datasets and evaluation metrics are analyzed.

Research challenges and future directions are identified.

Abstract

In recent times, data is growing rapidly in every domain such as news, social media, banking, education, etc. Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in original document without losing any critical purposes. Text summarization is emerged as an important research area in recent past. In this regard, review of existing work on text summarization process is useful for carrying out further research. In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction. This literature includes the discussion about different methodology used for keyword extraction and text summarization. It also discusses about different databases used for text summarization in several domains along with evaluation matrices. Finally, it discusses briefly about issues and research challenges faced by researchers along with future direction.