Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life.However,the enormous and complex nature of this content makes extracting valuable insi...Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life.However,the enormous and complex nature of this content makes extracting valuable insights challenging.Long document summarization emerges as a pivotal technique in this context,serving to distill extensive texts into concise and comprehensible summaries.This paper presents a novel three-stage pipeline for effective long document summarization.The proposed approach combines unsupervised and supervised learning techniques,efficiently handling large document sets while requiring minimal computational resources.Our methodology introduces a unique process for forming semantic chunks through spectral dynamic segmentation,effectively reducing redundancy and repetitiveness in the summarization process.Contrary to previous methods,our approach aligns each semantic chunk with the entire summary paragraph,allowing the abstractive summarization model to process documents without truncation and enabling the summarization model to deduce missing information from other chunks.To enhance the summary generation,we utilize a sophisticated rewrite model based on Bidirectional and Auto-Regressive Transformers(BART),rearranging and reformulating summary constructs to improve their fluidity and coherence.Empirical studies conducted on the long documents from the Webis-TLDR-17 dataset demonstrate that our approach significantly enhances the efficiency of abstractive summarization transformers.The contributions of this paper thus offer significant advancements in the field of long document summarization,providing a novel and effective methodology for summarizing extensive texts in the context of social media.展开更多
Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development tim...Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R＆D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification （IPC） codes B25C, B25D, B25F （categories for power hand tools） and B24B, C09G and H011 （categories for chemical mechanical polishing） are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.展开更多
文摘Social media’s explosive growth has resulted in a massive influx of electronic documents influencing various facets of daily life.However,the enormous and complex nature of this content makes extracting valuable insights challenging.Long document summarization emerges as a pivotal technique in this context,serving to distill extensive texts into concise and comprehensible summaries.This paper presents a novel three-stage pipeline for effective long document summarization.The proposed approach combines unsupervised and supervised learning techniques,efficiently handling large document sets while requiring minimal computational resources.Our methodology introduces a unique process for forming semantic chunks through spectral dynamic segmentation,effectively reducing redundancy and repetitiveness in the summarization process.Contrary to previous methods,our approach aligns each semantic chunk with the entire summary paragraph,allowing the abstractive summarization model to process documents without truncation and enabling the summarization model to deduce missing information from other chunks.To enhance the summary generation,we utilize a sophisticated rewrite model based on Bidirectional and Auto-Regressive Transformers(BART),rearranging and reformulating summary constructs to improve their fluidity and coherence.Empirical studies conducted on the long documents from the Webis-TLDR-17 dataset demonstrate that our approach significantly enhances the efficiency of abstractive summarization transformers.The contributions of this paper thus offer significant advancements in the field of long document summarization,providing a novel and effective methodology for summarizing extensive texts in the context of social media.
基金supported by National Science Council research grants
文摘Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R＆D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R＆D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification （IPC） codes B25C, B25D, B25F （categories for power hand tools） and B24B, C09G and H011 （categories for chemical mechanical polishing） are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.
