Boosting German News Articles with AI-Generated Content

Boosting German News Articles with AI-Generated Content

Table of Contents

1. Introduction
2. Goal of the Study
3. Analysis of Data
4. Extractive Summaries
5. Summaries Generation: GPT-3 vs Text DaVinci Model
6. Evaluation of Summaries Readability
7. Comparison to Human Summaries
8. Keynotes Generation
9. Questions and Answers Generation
10. Conclusion

Introduction

In this article, we will explore the topic of enriching German news articles with AI-generated content. We will delve into the process of generating summaries and questions and answers to add value to news articles. Our study focuses on data from the Swiss Daily Newspaper targets, specifically German articles. We will discuss the analysis of the data, the different models used for generating summaries, and the evaluation of the generated content's readability. Additionally, we will compare the AI-generated summaries to human-generated summaries and explore the generation of keynotes and questions with answers. Let's dive in!

Goal of the Study

The main goal of this study was to generate content that offers readers an added value for news articles. Two aspects were investigated in detail: the generation of summaries and the generation of questions and answers. The study utilized data from the Swiss Daily Newspaper targets, specifically German articles. The aim was to provide abstract summaries for five main categories: International, Swiss politics, syntax's item, Zurich, and economy. Let's explore how the study analyzed and processed this data.

Analysis of Data

The first step of the study involved analyzing the data collected, which included 4,000 German articles from the Swiss Daily Newspaper targets. These articles were categorized based on their frequency, and the study identified five main categories: International, Swiss politics, syntax's item, Zurich, and economy. The analysis also included the calculation of the average number of words per category. With the data analyzed, the study proceeded to generate extractive summaries using a baseline algorithm. This algorithm assigned higher weights to headline words and selected the highest-ranked sentences as summaries. However, the study also explored the use of other algorithms for generating more concise summaries.

Extractive Summaries

Extractive summaries involve taking sentences directly from the original text. For this study, the algorithm implemented weighted scores for each sentence, considering headline words more important. The highest-ranked sentences were selected as summaries. This approach was used for all the articles in the database. Extractive summaries served as a baseline for comparing the effectiveness of other algorithms. The study also aimed to generate abstractive summaries, which involve rewriting sentences from scratch.