# Text Understanding with the Attention Sum Reader Network

arXiv: https://arxiv.org/abs/1603.01547 · HTML: https://mlsanity.com/paper/1603.01547/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2016.

## Built on by (most cited first, 18 of 18)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising](https://mlsanity.com/paper/2105.07911/) (2021)
- [Learning to Rehearse in Long Sequence Memorization](https://mlsanity.com/paper/2106.01096/) (2021)
- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)
- [Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models](https://mlsanity.com/paper/2108.11574/) (2021)
- [ExpMRC: Explainability Evaluation for Machine Reading Comprehension](https://mlsanity.com/paper/2105.04126/) (2021)
- [Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering](https://mlsanity.com/paper/2206.12866/) (2022)
- [A Coarse to Fine Question Answering System based on Reinforcement Learning](https://mlsanity.com/paper/2106.00257/) (2021)
- [A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections](https://mlsanity.com/paper/2106.08908/) (2021)
- [To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://mlsanity.com/paper/2208.01299/) (2022)
- [A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information](https://mlsanity.com/paper/2305.07565/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [Orthogonal Attention: A Cloze-Style Approach to Negation Scope Resolution](https://mlsanity.com/paper/2103.04294/) (2021)
- [Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension](https://mlsanity.com/paper/2107.07957/) (2021)
- [Multi Document Reading Comprehension](https://mlsanity.com/paper/2201.01706/) (2022)
- [Feature-augmented Machine Reading Comprehension with Auxiliary Tasks](https://mlsanity.com/paper/2211.09438/) (2022)
- [Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2301.00429/) (2023)

## Abstract (arXiv)

Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Ensemble of our models sets new state of the art on all evaluated datasets.