Quiz-Style Question Generation for News Stories

TL;DR

This paper introduces a new dataset and methods for generating quiz-style questions from news articles to assess and improve news informedness, demonstrating effective model performance and positive user feedback.

Contribution

It presents the NewsQuizQA dataset and novel Transformer-based techniques for question-answer and distractor generation from news summaries.

Findings

Models outperform baselines on automated metrics.

Generated questions are rated as educational and enjoyable by users.

Weekly quizzes engaged real users over two months.

Abstract

A large majority of American adults get at least some of their news from the Internet. Even though many online news products have the goal of informing their users about the news, they lack scalable and reliable tools for measuring how well they are achieving this goal, and therefore have to resort to noisy proxy metrics (e.g., click-through rates or reading time) to track their performance. As a first step towards measuring news informedness at a scale, we study the problem of quiz-style multiple-choice question generation, which may be used to survey users about their knowledge of recent news. In particular, we formulate the problem as two sequence-to-sequence tasks: question-answer generation (QAG) and distractor, or incorrect answer, generation (DG). We introduce NewsQuizQA, the first dataset intended for quiz-style question-answer generation, containing 20K human written question-answer pairs from 5K news article summaries. Using this dataset, we propose a series of novel techniques for applying large pre-trained Transformer encoder-decoder models, namely PEGASUS and T5, to the tasks of question-answer generation and distractor generation. We show that our models outperform strong baselines using both automated metrics and human raters. We provide a case study of running weekly quizzes on real-world users via the Google Surveys platform over the course of two months. We found that users generally found the automatically generated questions to be educational and enjoyable. Finally, to serve the research community, we are releasing the NewsQuizQA dataset.