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Refocusing on Relevance: Personalization in NLG.

Shiran Dudy1, Steven Bedrick2, Bonnie Webber3

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Summary
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Natural language generation (NLG) systems need more context beyond source text for better user-oriented tasks. Incorporating relevance from information retrieval is key, alongside value-sensitive design to mitigate potential harms.

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Area of Science:

  • Natural Language Processing
  • Human-Computer Interaction
  • Information Retrieval

Background:

  • Current natural language generation (NLG) systems often rely solely on source text.
  • This approach is insufficient when user intent or work context is not explicit in the source text.
  • Such limitations hinder the effectiveness of NLG in real-world applications.

Purpose of the Study:

  • To advocate for increased emphasis on contextual information in NLG system design.
  • To propose the concept of 'relevance' from Information Retrieval as a core principle for user-oriented NLG.
  • To explore potential harms of context-aware NLG and suggest value-sensitive design as a solution.

Main Methods:

  • Conceptual analysis of existing NLG paradigms.
  • Re-framing NLG task design through the lens of Information Retrieval relevance.
  • Discussion of ethical considerations and design principles for personalized NLG.

Main Results:

  • Standard NLG methods are inadequate for context-dependent tasks.
  • Relevance is a critical factor for developing user-centric NLG.
  • Personalization in NLG presents ethical challenges requiring careful consideration.

Conclusions:

  • NLG systems must integrate broader context for improved performance.
  • Information Retrieval's relevance metric offers a valuable framework for context-aware NLG.
  • Value-sensitive design is essential for navigating the ethical landscape of personalized text generation.