RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents
This addresses the need for automated quote extraction in applications like news and essays, but it is incremental as it builds on existing retrieval and multi-task learning techniques.
The paper tackled the problem of extracting inspirational quotes from long documents by proposing a retrieval-augmented multi-task reader approach, achieving a maximum improvement of 5.08% in BoW F1-score over state-of-the-art methods.
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 extract 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 datasets and introduce a novel multi-task framework RA-MTR that improves the state-of-the-art performance, achieving a maximum improvement of 5.08% in BoW F1-score.