Text Summarization in the Biomedical Domain

by   Milad Moradi, et al.

This chapter gives an overview of recent advances in the field of biomedical text summarization. Different types of challenges are introduced, and methods are discussed concerning the type of challenge that they address. Biomedical literature summarization is explored as a leading trend in the field, and some future lines of work are pointed out. Underlying methods of recent summarization systems are briefly explained and the most significant evaluation results are mentioned. The primary purpose of this chapter is to review the most significant research efforts made in the current decade toward new methods of biomedical text summarization. As the main parts of this chapter, current trends are discussed and new challenges are introduced.


page 1

page 2

page 3

page 4


Evaluation of Automatic Text Summarization using Synthetic Facts

Despite some recent advances, automatic text summarization remains unrel...

Deep Algorithm Unrolling for Biomedical Imaging

In this chapter, we review biomedical applications and breakthroughs via...

Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which...

Different approaches for identifying important concepts in probabilistic biomedical text summarization

Automatic text summarization tools help users in biomedical domain to ac...

Automatic Keyword Extraction for Text Summarization: A Survey

In recent times, data is growing rapidly in every domain such as news, s...

SuMe: A Dataset Towards Summarizing Biomedical Mechanisms

Can language models read biomedical texts and explain the biomedical mec...

Searching for Effective Neural Extractive Summarization: What Works and What's Next

The recent years have seen remarkable success in the use of deep neural ...