Unveiling Wisdom: Inspirational Quote Extraction using a Retrieval Augmented Multi-Task Reader
Abstract: Inspirational quotes from famous individuals are often used to convey thoughts in news articles, essays, and everyday conversations. In this paper, we propose a novel context-based quote extraction system that aims to predict the most relevant quote from a long text. We formulate this quote extraction as an open domain question answering problem first by employing a vector-store based retriever and then applying a multi-task reader. We curate three context-based quote extraction dataset and introduce a novel multi-task framework that improves the state-of-the-art performance, achieving a maximum improvement of 5.08% in BoW F1-score.
Paper Type: Long
Research Area: Information Extraction
Research Area Keywords: Context-aware quote extraction, RAG, NLP application
Contribution Types: NLP engineering experiment, Data resources
Languages Studied: English
Submission Number: 4730
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