Abstractive-based Text Summarization Algorithm
(Redirected from abstractive summarization)
Jump to navigation
Jump to search
An Abstractive-based Text Summarization Algorithm is a text summarization algorithm that can significantly transform the underlying text.
- Context:
- It can be implemented by an Abstractive Summarization System to solve an abstractive summarization task.
- It can range from being a Single-Document Abstractive Summarization Algorithm to being a Multi-Document Abstractive Summarization Algorithm.
- It can range from being a General Abstractive Summarization Algorithm to being a Topic-focused Abstractive Summarization Algorithm.
- …
- Counter-Example(s):
- See: MEAD Algorithm.
References
2022b
- (Ghadimi & Beigy, 2022) ⇒ Alireza Ghadimi, and Hamid Beigy. (2022). “Hybrid Multi-document Summarization Using Pre-trained Language Models.” Expert Systems with Applications 192
- ABSTRACT: Abstractive multi-document summarization is a type of automatic text summarization. It obtains information from multiple documents and generates a human-like summary from them. In this paper, we propose an abstractive multi-document summarization method called HMSumm. The proposed method is a combination of extractive and abstractive summarization approaches. First, it constructs an extractive summary from multiple input documents, and then uses it to generate the abstractive summary. Redundant information, which is a global problem in multi-document summarization, is managed in the first step. Specifically, the determinantal point process (DPP) is used to deal with redundancy. This step also controls the length of the input sequence for the abstractive summarization process. This step has two effects: The first is to reduce the computational time, and the second is to preserve the important parts of the input documents for an abstractive summarizer. We employ a deep submodular network (DSN) to determine the quality of the sentences in the extractive summary, and use BERT-based similarityies to compute the redundancy. The obtained extractive summary is fed into BART and T5 pre-trained models to generate two abstractive summaries. We use the diversity of sentences in each summary to select one of them as the final abstractive summary. To evaluate the performance of HMSumm, we use both human evaluations and ROUGE-based assessments, and compare it with several state-of-the-art methods. We use DUC 2002, DUC 2004, Multi-News, and CNN/DailyMail datasets to evaluate the algorithms. The experimental results show that HMSumm outperforms the related state-of-the-art algorithms.
2018a
- (Paulus et al., 2017) ⇒ Romain Paulus, Caiming Xiong, and Richard Socher. (2017). “A Deep Reinforced Model for Abstractive Summarization.” In: Proceedings of ICLR 2018 Conference (ICLR 2018).
2018b
- (Liu et al., 2018) ⇒ Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. (2018). “Generating Wikipedia by Summarizing Long Sequences.” In: Proceedings of the Sixth International Conference on Learning Representations (ICLR-2018).
- QUOTE: We show that generating English Wikipedia articles can be approached as a multi-document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. …
2017
- (See et al., 2017) ⇒ Abigail See, Peter J. Liu, and Christopher D. Manning. (2017). “Get To The Point: Summarization with Pointer-Generator Networks.” In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). DOI:10.18653/v1/P17-1099.
- QUOTE: ... Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text) …
2015
- (Rush et al., 2015) ⇒ Alexander M. Rush, Sumit Chopra, and Jason Weston. (2015). “A Neural Attention Model for Abstractive Sentence Summarization.” In: Proceedings of EMNLP-2015.
- QUOTE: Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. …