A Combined Extractive With Abstractive Model for Summarization
Aiming at the difficulties in document-level summarization, this paper presents a two-stage, extractive and then abstractive summarization model. In the first stage, we extract the important sentences by combining sentences similarity matrix (only used for the first time) or pseudo-title, which take...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9380377/ |
| _version_ | 1811274208278216704 |
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| author | Wenfeng Liu Yaling Gao Jinming Li Yuzhen Yang |
| author_facet | Wenfeng Liu Yaling Gao Jinming Li Yuzhen Yang |
| author_sort | Wenfeng Liu |
| collection | DOAJ |
| description | Aiming at the difficulties in document-level summarization, this paper presents a two-stage, extractive and then abstractive summarization model. In the first stage, we extract the important sentences by combining sentences similarity matrix (only used for the first time) or pseudo-title, which takes full account of the features (such as sentence position, paragraph position, and more.). To extract coarse-grained sentences from a document, and considers the sentence differentiation for the most important sentences in the document. The second stage is abstractive, and we use beam search algorithm to restructure and rewrite these syntactic blocks of these extracted sentences. Newly generated summary sentence serves as the pseudo-summary of the next round. Globally optimal pseudo-title acts as the final summarization. Extensive experiments have been performed on the corresponding data set, and the results show our model can obtain better results. |
| first_indexed | 2024-04-12T23:14:46Z |
| format | Article |
| id | doaj.art-31debad9c24c4d7ba511d581525b0e27 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:14:46Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31debad9c24c4d7ba511d581525b0e272022-12-22T03:12:42ZengIEEEIEEE Access2169-35362021-01-019439704398010.1109/ACCESS.2021.30664849380377A Combined Extractive With Abstractive Model for SummarizationWenfeng Liu0https://orcid.org/0000-0001-7568-3844Yaling Gao1Jinming Li2Yuzhen Yang3School of Computer, Heze University, Heze, ChinaSchool of Computer, Heze University, Heze, ChinaSchool of Computer, Heze University, Heze, ChinaSchool of Computer, Heze University, Heze, ChinaAiming at the difficulties in document-level summarization, this paper presents a two-stage, extractive and then abstractive summarization model. In the first stage, we extract the important sentences by combining sentences similarity matrix (only used for the first time) or pseudo-title, which takes full account of the features (such as sentence position, paragraph position, and more.). To extract coarse-grained sentences from a document, and considers the sentence differentiation for the most important sentences in the document. The second stage is abstractive, and we use beam search algorithm to restructure and rewrite these syntactic blocks of these extracted sentences. Newly generated summary sentence serves as the pseudo-summary of the next round. Globally optimal pseudo-title acts as the final summarization. Extensive experiments have been performed on the corresponding data set, and the results show our model can obtain better results.https://ieeexplore.ieee.org/document/9380377/Extractive summarizationabstractive summarizationbeam searchword embeddings |
| spellingShingle | Wenfeng Liu Yaling Gao Jinming Li Yuzhen Yang A Combined Extractive With Abstractive Model for Summarization IEEE Access Extractive summarization abstractive summarization beam search word embeddings |
| title | A Combined Extractive With Abstractive Model for Summarization |
| title_full | A Combined Extractive With Abstractive Model for Summarization |
| title_fullStr | A Combined Extractive With Abstractive Model for Summarization |
| title_full_unstemmed | A Combined Extractive With Abstractive Model for Summarization |
| title_short | A Combined Extractive With Abstractive Model for Summarization |
| title_sort | combined extractive with abstractive model for summarization |
| topic | Extractive summarization abstractive summarization beam search word embeddings |
| url | https://ieeexplore.ieee.org/document/9380377/ |
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