Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models

TL;DR

This paper introduces a new task of generating a common question from multiple documents using a multi-source encoder-decoder model, demonstrating improved performance over baselines on the MS-MARCO-QA dataset.

Contribution

It proposes a simple multi-source question generation framework that effectively captures common concepts across documents, advancing multi-document question generation techniques.

Findings

Outperforms baseline models on automated metrics

Achieves higher human judgment scores

Effectively captures common concepts across documents

Abstract

Ambiguous user queries in search engines result in the retrieval of documents that often span multiple topics. One potential solution is for the search engine to generate multiple refined queries, each of which relates to a subset of the documents spanning the same topic. A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents. We propose a new task of generating common question from multiple documents and present simple variant of an existing multi-source encoder-decoder framework, called the Multi-Source Question Generator (MSQG). We first train an RNN-based single encoder-decoder generator from (single document, question) pairs. At test time, given multiple documents, the 'Distribute' step of our MSQG model predicts target word distributions for each document using the trained model. The 'Aggregate' step aggregates these distributions to generate a common question. This simple yet effective strategy significantly outperforms several existing baseline models applied to the new task when evaluated using automated metrics and human judgments on the MS-MARCO-QA dataset.