Label-Free Topic-Focused Summarization Using Query Augmentation
In today's data and information-rich world, summarization techniques are essential in harnessing vast text to extract key information and enhance decision-making and efficiency. In particular, topic-focused summarization is important due to its ability to tailor content to specific aspects of an extended text. However, this usually requires extensive labelled datasets and considerable computational power. This study introduces a novel method, Augmented-Query Summarization (AQS), for topic-focused summarization without the need for extensive labelled datasets, leveraging query augmentation and hierarchical clustering. This approach facilitates the transferability of machine learning models to the task of summarization, circumventing the need for topic-specific training. Through real-world tests, our method demonstrates the ability to generate relevant and accurate summaries, showing its potential as a cost-effective solution in data-rich environments. This innovation paves the way for broader application and accessibility in the field of topic-focused summarization technology, offering a scalable, efficient method for personalized content extraction.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization
Both supervised learning methods and LDA based topic model have been successfully applied in the field of query focused multi-document summarization. In this paper, we propose a novel supervised approach that can incorpo…
Document SummarizationMulti-Document SummarizationSentenceTopic ModelsQuery-focused Multi-document Summarization: Combining a Novel Topic Model with Graph-based Semi-supervised Learning
Graph-based semi-supervised learning has proven to be an effective approach for query-focused multi-document summarization. The problem of previous semi-supervised learning is that sentences are ranked without considerin…
Document SummarizationMulti-Document SummarizationSentenceQuery-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning
Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
Large-scale datasets are widely used to perform summarization tasks, but they may not include queries alongside documents and summaries. In the search for suitable datasets for Query-Focused Summarization (QFS), we ident…
QFS-Composer: Query-focused summarization pipeline for less resourced languages
Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with restricted training resources. This work addresses the challenge of que…
Question GenerationText SummarizationQuestion Answering