Targeted Concept Simplification System

A Targeted Concept Simplification System is a natural language processing system designed to automatically and systematically perform targeted concept simplification tasks.

• AKA: Concept-Focused Simplification System, Contextual Concept Explanation System, Localized Concept Rewriting System, Concept Simplification System.
• Context:
  • It utilizes algorithms and models such as:
    • Lexical Simplification Algorithms: For substituting complex terms with simpler synonyms.
    • Contextual Explanation Models: To provide explanations based on the surrounding text.
    • Paraphrasing Techniques: For rephrasing complex sentences while retaining meaning.
    • ...
  • It can perform concept extraction, transforming complex ideas into simpler, understandable forms.
  • It can support automated summarization of technical documents by identifying key concepts.
  • It can assist in ontology creation by identifying hierarchical relationships between concepts.
  • It can range from being a rule-based system (implementing predefined rules for simplification) to a sophisticated AI-powered system, depending on the complexity of the task.
  • It can be integrated with text processing systems and knowledge management systems for broader applications.
  • It can improve learning and understanding by breaking down complex ideas for users.
  • ...
• Example(s):
  • Text Simplification Systems, which transform complex text into simpler language for educational purposes.
  • Ontology Simplification Systems, which organize and simplify domain-specific knowledge for research.
  • Technical Document Summarization Systems, which extract key concepts from complex technical manuals.
  • ...
• Counter-Example(s):
  • Information Retrieval Systems, which focus on finding relevant information rather than simplifying concepts.
  • Text Classification Systems, which categorize text without altering its complexity.
  • Named Entity Recognition Systems, which identify entities but do not simplify the content.
  • ...
• See: Text Simplification Task, Ontology Simplification Task, Natural Language Processing Task, Knowledge Management System, WikiDomains Dataset.

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References

2024a

• (Asthana et al., 2024) ⇒ Sumit Asthana, Hannah Rashkin, Elizabeth Clark, Fantine Huot, & Mirella Lapata. (2024). "Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts". In: Proceedings of EMNLP 2024.
  • QUOTE: "Targeted concept simplification addresses adult readers' difficulties with domain-specific texts by rewriting complex concepts while preserving contextual details.

    The WikiDomains dataset contains 22k definitions from 13 academic domains, each annotated with a difficult concept requiring simplification.

    Human evaluations revealed low correlation (∼0.2) between automated metrics and judgments of concept simplification quality, highlighting challenges in personalized reading support."

2024b

• (Asthana et al., 2024) ⇒ Sumit Asthana, Hannah Rashkin, Elizabeth Clark, Fantine Huot, & Mirella Lapata. (2024). "Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts". arXiv Preprint.
  • QUOTE: "Language models outperformed dictionary baselines in meaning preservation but struggled with explanation depth required for domain concept comprehension.

    Reader preference studies showed 58% favor concept explanations over direct simplifications in scientific texts."

2024c

• (Google DeepMind, 2024) ⇒ Google DeepMind. (2024). "WikiDomains Dataset".
  • QUOTE: "WikiDomains dataset contains 15,873 training examples and 3,304 test examples across 13 domains including Biology, Physics, and Politics & Government.

    Each entry includes term definition, difficult concept phrase, and domain classification with Wikidata IDs for entity disambiguation."

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