Decision Support by Means of Automatically Extracting Natural Language Arguments from Big Data

Motivation

In order to make informed decisions, appropriate arguments are needed. However, the mere amount of information and the complexity of many questions frequently prevents us from finding all arguments that are relevant for a reasonable decision. Within the "Decision support by means of automatically extracting natural language arguments from big data" (in short, ArgumenText) project, the UKP Lab develops novel Argument Mining methods for extracting arguments from large and heterogeneous text sources in order to facilitate decision making processes.

In response to a user-defined search query, neural networks determine relevant arguments in realtime and summarize them in a comprehensive way. In contrast to conventional systems, an argumentative information system can show the reasons for or against a decision.

Goals

The key features of our envisioned argument mining system include:

  • Automatic extraction of arguments from large amounts of heterogeneous texts
  • Detecting pro and con arguments
  • Real-time summarization in natural language
  • Language adaptation of argument mining to German

In addition to supporting decision-making, our argumentative information system will provide an overview of current topics. Its real-time summary of natural language arguments simplifies a wide range of knowledge generation processes in areas such as journalism, politics, and economics.

Methods

In this project, we aim to validate argument mining methods for heterogeneous and large scale texts. Starting from a previously developed joint-modeling method for identifying argument structures in student essays (Stab and Gurevych 2017), we will develop robust end-to-end approaches for mining arguments from web-scale corpora. We also seek to extent our current methods to German using Language Adaptation and evaluate their robustness across various topics and text types. The resulting methods and software are intended to extract arguments from dynamic text sources in real-time and to prepare them for the user in a comprehensive summary. 

 

Funding


The project is funded by the German Federal Ministry of Education and Research (BMBF) as part of the VIP+ program ("Validierung des technologischen und gesellschaftlichen Innovationspotenzials wissenschaftlicher Forschung").

 

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