Large-Scale Multi-Document Summarization with Information Extraction and Compression
We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Unlike existing multi-document summarization methods, our framework processes documents telling different stories instead of documents on the same topic. We also enhance an existing sentence fusion method with a uni-directional language model to prioritize fused sentences with higher sentence probability with the goal of increasing readability. Lastly, we construct a total of twelve dataset variations based on CNN/Daily Mail and the NewsRoom datasets, where each document group contains a large and diverse collection of documents to evaluate the performance of our model in comparison with other baseline systems. Our experiments demonstrate that our framework outperforms current state-of-the-art methods in this more generic setting.
Code (0)
등록된 구현이 없습니다.
Tasks
Abstractive Text SummarizationDocument SummarizationLanguage ModelingLanguage ModellingMulti-Document SummarizationSentenceSentence FusionSimilar Papers 제목 키워드 기반
REDTABS: A Collection of Report Document Datasets for Long Text and Multi-Table Summarization
Automatic document summarization aims to produce a concise summary covering the input document's salient content. Within a report document, both the textual and non-textual content (e.g., tables and figures) can be impo…
Document SummarizationInformativenessText GenerationMulti-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi…
ArticlesDescriptiveDocument SummarizationExtreme Summarization+1Neural Extractive Summarization with Side Information
Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions …
ArticlesDocument SummarizationExtractive SummarizationImage Captioning+1Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study
Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied…
Abstractive Text SummarizationDocument SummarizationMachine TranslationMulti-Document Summarization+1Towards a Neural Network Approach to Abstractive Multi-Document Summarization
Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied…
Abstractive Text SummarizationDocument SummarizationMulti-Document Summarization