Extractive Summarization Using Multi-Task Learning with Document Classification
The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multi-task learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDocument ClassificationDocument SummarizationExtractive SummarizationGeneral ClassificationMulti-Task LearningSentenceSimilar Papers 제목 키워드 기반
Imbalanced multi-label classification using multi-task learning with extractive summarization
Extractive summarization and imbalanced multi-label classification often require vast amounts of training data to avoid overfitting. In situations where training data is expensive to generate, leveraging information betw…
ClassificationExtractive SummarizationGeneral ClassificationMulti-Label Classification+3Extractive Summarization: Limits, Compression, Generalized Model and Heuristics
Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall …
Document SummarizationExtractive SummarizationmodelMulti-Document Summarization+1CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization
Our system participates in two shared tasks, CL-SciSumm 2020 and LongSumm 2020. In the CL-SciSumm shared task, based on our previous work, we apply more machine learning methods on position features and content features …
Abstractive Text SummarizationDocument SummarizationExtractive SummarizationPosition+1Document Summarization with Text Segmentation
In this paper, we exploit the innate document segment structure for improving the extractive summarization task. We build two text segmentation models and find the most optimal strategy to introduce their output predicti…
ArticlesDocument SummarizationExtractive SummarizationSegmentation+2Supervising the Centroid Baseline for Extractive Multi-Document Summarization
The centroid method is a simple approach for extractive multi-document summarization and many improvements to its pipeline have been proposed. We further refine it by adding a beam search process to the sentence selectio…
Document SummarizationMulti-Document SummarizationSentence