# More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering

Source: [More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering](<https://www.sota2.com/research/paper/68fa6bb61e209131cea2fc8e>)

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

Data updated: 2025-10-25T23:55:05.513Z

Authors: Yang Bai, Daisy Zhe Wang

Published: 2021-09-25T02:36:53.000Z

## Abstract

Textual Question Answering (QA) aims to provide precise answers to user's questions in natural language using unstructured data. One of the most popular approaches to this goal is machine reading comprehension(MRC). In recent years, many novel datasets and evaluation metrics based on classical MRC tasks have been proposed for broader textual QA tasks. In this paper, we survey 47 recent textual QA benchmark datasets and propose a new taxonomy from an application point of view. In addition, We summarize 8 evaluation metrics of textual QA tasks. Finally, we discuss current trends in constructing textual QA benchmarks and suggest directions for future work.

## 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/2109.12264>)

[PDF](<https://arxiv.org/pdf/2109.12264.pdf>)