# Question Answering as an Automatic Evaluation Metric for News Article Summarization

Source: [Question Answering as an Automatic Evaluation Metric for News Article Summarization](<https://www.sota2.com/research/paper/68fa65d61e209131cea0c926>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:36:37.771Z

Authors: Matan Eyal, Tal Baumel, Michael Elhadad

Published: 2019-06-02T00:29:05.000Z

## Abstract

Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. APES utilizes recent progress in the field of reading-comprehension to quantify the ability of a summary to answer a set of manually created questions regarding central entities in the source article. We first analyze the strength of this metric by comparing it to known manual evaluation metrics. We then present an end-to-end neural abstractive model that maximizes APES, while increasing ROUGE scores to competitive results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.00318>)

[PDF](<https://arxiv.org/pdf/1906.00318.pdf>)