Automatic Summarization System coupled with a Question-Answering System (QAAS)

TL;DR

This paper presents a system that combines automatic summarization with question-answering to improve answer accuracy, tested on French multi-document corpora with promising results.

Contribution

It introduces a coupled summarization and question-answering system that adapts summaries to specific questions, enhancing performance over previous methods.

Findings

Coupling summarization with QA improves answer precision.

High compression summarization enhances noise filtering.

Personalized summaries yield better QA performance.

Abstract

To select the most relevant sentences of a document, it uses an optimal decision algorithm that combines several metrics. The metrics processes, weighting and extract pertinence sentences by statistical and informational algorithms. This technique might improve a Question-Answering system, whose function is to provide an exact answer to a question in natural language. In this paper, we present the results obtained by coupling the Cortex summarizer with a Question-Answering system (QAAS). Two configurations have been evaluated. In the first one, a low compression level is selected and the summarization system is only used as a noise filter. In the second configuration, the system actually functions as a summarizer, with a very high level of compression. Our results on French corpus demonstrate that the coupling of Automatic Summarization system with a Question-Answering system is promising. Then the system has been adapted to generate a customized summary depending on the specific question. Tests on a french multi-document corpus have been realized, and the personalized QAAS system obtains the best performances.