QontSum: On Contrasting Salient Content for Query-focused Summarization

• URL: http://arxiv.org/abs/2307.07586v1
• Date: Fri, 14 Jul 2023 19:25:35 GMT
• Title: QontSum: On Contrasting Salient Content for Query-focused Summarization
• Authors: Sajad Sotudeh, Nazli Goharian
• Abstract summary: Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. This paper highlights the role of QFS in Grounded Answer Generation (GAR) We propose QontSum, a novel approach for QFS that leverages contrastive learning to help the model attend to the most relevant regions of the input document.
• Score: 22.738731393540633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. The broader field of Generative Information Retrieval (Gen-IR) aims to revolutionize information extraction from vast document corpora through generative approaches, encompassing Generative Document Retrieval (GDR) and Grounded Answer Retrieval (GAR). This paper highlights the role of QFS in Grounded Answer Generation (GAR), a key subdomain of Gen-IR that produces human-readable answers in direct correspondence with queries, grounded in relevant documents. In this study, we propose QontSum, a novel approach for QFS that leverages contrastive learning to help the model attend to the most relevant regions of the input document. We evaluate our approach on a couple of benchmark datasets for QFS and demonstrate that it either outperforms existing state-of-the-art or exhibits a comparable performance with considerably reduced computational cost through enhancements in the fine-tuning stage, rather than relying on large-scale pre-training experiments, which is the focus of current SOTA. Moreover, we conducted a human study and identified improvements in the relevance of generated summaries to the posed queries without compromising fluency. We further conduct an error analysis study to understand our model's limitations and propose avenues for future research.

Related papers

• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• KaPQA: Knowledge-Augmented Product Question-Answering [59.096607961704656]
  We introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products. We also propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task.
  arXiv  Detail & Related papers  (2024-07-22T22:14:56Z)
• IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization [59.06663981902496]
  Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. We investigate two indispensable characteristics that the LLMs-based QFS models should be harnessed, Lengthy Document Summarization and Efficiently Fine-grained Query-LLM Alignment. These innovations pave the way for broader application and accessibility in the field of QFS technology.
  arXiv  Detail & Related papers  (2024-07-15T07:14:56Z)
• A Survey on Retrieval-Augmented Text Generation for Large Language Models [1.4579344926652844]
  Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements. This paper organizes the RAG paradigm into four categories: pre-retrieval, retrieval, post-retrieval, and generation. It outlines RAG's evolution and discusses the field's progression through the analysis of significant studies.
  arXiv  Detail & Related papers  (2024-04-17T01:27:42Z)
• RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation [42.82192656794179]
  Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pretraining datasets, making them susceptible to errors in unseen scenarios. Retrieval-Augmented Generation (RAG) addresses this by incorporating external, relevant documents into the response generation process.
  arXiv  Detail & Related papers  (2024-03-31T08:58:54Z)
• REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering [122.62012375722124]
  In existing methods, large language models (LLMs) cannot precisely assess the relevance of retrieved documents. We propose REAR, a RElevance-Aware Retrieval-augmented approach for open-domain question answering (QA)
  arXiv  Detail & Related papers  (2024-02-27T13:22:51Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance [43.820971952979875]
  We propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model. Our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-27T06:58:42Z)
• Abstractive Query Focused Summarization with Query-Free Resources [60.468323530248945]
  In this work, we consider the problem of leveraging only generic summarization resources to build an abstractive QFS system. We propose Marge, a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries. Despite learning from minimal supervision, our system achieves state-of-the-art results in the distantly supervised setting.
  arXiv  Detail & Related papers  (2020-12-29T14:39:35Z)
• EQG-RACE: Examination-Type Question Generation [21.17100754955864]
  We propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines.
  arXiv  Detail & Related papers  (2020-12-11T03:52:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.