# S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1706.04815 · HTML: https://mlsanity.com/paper/1706.04815/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [Rethinking Search: Making Domain Experts out of Dilettantes](https://mlsanity.com/paper/2105.02274/) (2021)
- [Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering](https://mlsanity.com/paper/2103.06500/) (2021)
- [Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset](https://mlsanity.com/paper/2204.09659/) (2022)
- [Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts](https://mlsanity.com/paper/2210.01750/) (2022)
- [KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information](https://mlsanity.com/paper/2301.07341/) (2023)
- [ViLegalExpert: A Large-Scale Benchmark for Vietnamese Legal Retrieval and Question Answering from Real-World Consultations](https://mlsanity.com/paper/2609.39189/) (2026)

## Abstract (arXiv)

In this paper, we present a novel approach to machine reading comprehension for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a question with exact text spans in a passage, the MS-MARCO dataset defines the task as answering a question from multiple passages and the words in the answer are not necessary in the passages. We therefore develop an extraction-then-synthesis framework to synthesize answers from extraction results. Specifically, the answer extraction model is first employed to predict the most important sub-spans from the passage as evidence, and the answer synthesis model takes the evidence as additional features along with the question and passage to further elaborate the final answers. We build the answer extraction model with state-of-the-art neural networks for single passage reading comprehension, and propose an additional task of passage ranking to help answer extraction in multiple passages. The answer synthesis model is based on the sequence-to-sequence neural networks with extracted evidences as features. Experiments show that our extraction-then-synthesis method outperforms state-of-the-art methods.