mlsanityData to 7 Oct 2026

RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents

arXiv 2502.12124 ↗First posted 17 Feb 2025cs.CLcited by 0

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 20 of its 34 references that are in the map. First posted on arXiv 17 Feb 2025.

Builds on

20 of 34 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  6. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  7. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  8. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,041
  9. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  10. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
  11. 2019Multi-Stage Document Ranking with BERTcited by 200
  12. 2019Deeper Text Understanding for IR with Contextual Neural Language Modelingcited by 111
8 more
  1. 2019Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answeringcited by 96
  2. 2018Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answeringcited by 24
  3. 2022QuoteR: A Benchmark of Quote Recommendation for Writingcited by 10
  4. 2015QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patternscited by 8
  5. 2021Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologiescited by 7
  6. 2021Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotationscited by 5
  7. 2020Context-Based Quotation Recommendationcited by 4
  8. 2021News Article Retrieval in Context for Event-centric Narrative Creationcited by 3

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  1. 2025LLMQuoter: Enhancing RAG Capabilities Through Efficient Quote Extraction From Large Contexts
  2. 2025QuOTE: Question-Oriented Text Embeddings
  3. 2020Catching Attention with Automatic Pull Quote Selection
  4. 2025Conversational Text Extraction with Large Language Models Using Retrieval-Augmented Systems
  5. 2025Say Less, Mean More: Leveraging Pragmatics in Retrieval-Augmented Generation
  6. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
  7. 2025Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization
  8. 2025ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation