Automated News Summarization Using Transformers

TL;DR

This paper compares transformer-based pre-trained models for automated news summarization, highlighting their effectiveness in generating concise summaries from large text datasets like BBC news.

Contribution

It provides a comprehensive comparison of transformer architectures for extractive and abstractive summarization using a standard news dataset.

Findings

Transformer models outperform traditional methods in summarization quality.

Pre-trained models significantly reduce manual effort in generating summaries.

Abstractive methods produce more fluent summaries than extractive ones.

Abstract

The amount of text data available online is increasing at a very fast pace hence text summarization has become essential. Most of the modern recommender and text classification systems require going through a huge amount of data. Manually generating precise and fluent summaries of lengthy articles is a very tiresome and time-consuming task. Hence generating automated summaries for the data and using it to train machine learning models will make these models space and time-efficient. Extractive summarization and abstractive summarization are two separate methods of generating summaries. The extractive technique identifies the relevant sentences from the original document and extracts only those from the text. Whereas in abstractive summarization techniques, the summary is generated after interpreting the original text, hence making it more complicated. In this paper, we will be presenting a comprehensive comparison of a few transformer architecture based pre-trained models for text summarization. For analysis and comparison, we have used the BBC news dataset that contains text data that can be used for summarization and human generated summaries for evaluating and comparing the summaries generated by machine learning models.