Improving Abstractive Dialogue Summarization Using Keyword Extraction
Abstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/17/9771 |
| _version_ | 1797582828403163136 |
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| author | Chongjae Yoo Hwanhee Lee |
| author_facet | Chongjae Yoo Hwanhee Lee |
| author_sort | Chongjae Yoo |
| collection | DOAJ |
| description | Abstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts. Moreover, the system often generates uninformative summaries. In this paper, we propose a novel keyword-aware dialogue summarization system (KADS) that easily captures the subject in the dialogue to alleviate the problem mentioned above through the efficient usage of keywords. Specifically, we first extract the keywords from the input dialogue using a pre-trained keyword extractor. Subsequently, KADS efficiently leverages the keywords information of the dialogue to the transformer-based dialogue system by using the pre-trained keyword extractor. Extensive experiments performed on three benchmark datasets show that the proposed method outperforms the baseline system. Additionally, we demonstrate that the proposed keyword-aware dialogue summarization system exhibits a high-performance gain in low-resource conditions where the number of training examples is highly limited. |
| first_indexed | 2024-03-10T23:28:05Z |
| format | Article |
| id | doaj.art-d0b502ed21a94df6b688f676b3b240ad |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:28:05Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d0b502ed21a94df6b688f676b3b240ad2023-11-19T07:51:33ZengMDPI AGApplied Sciences2076-34172023-08-011317977110.3390/app13179771Improving Abstractive Dialogue Summarization Using Keyword ExtractionChongjae Yoo0Hwanhee Lee1LG Electronics, Seoul 06772, Republic of KoreaDepartment of Artificial Intelligence, Chung-Ang University, Seoul 06974, Republic of KoreaAbstractive dialogue summarization aims to generate a short passage that contains important content for a particular dialogue spoken by multiple speakers. In abstractive dialogue summarization systems, capturing the subject in the dialogue is challenging owing to the properties of colloquial texts. Moreover, the system often generates uninformative summaries. In this paper, we propose a novel keyword-aware dialogue summarization system (KADS) that easily captures the subject in the dialogue to alleviate the problem mentioned above through the efficient usage of keywords. Specifically, we first extract the keywords from the input dialogue using a pre-trained keyword extractor. Subsequently, KADS efficiently leverages the keywords information of the dialogue to the transformer-based dialogue system by using the pre-trained keyword extractor. Extensive experiments performed on three benchmark datasets show that the proposed method outperforms the baseline system. Additionally, we demonstrate that the proposed keyword-aware dialogue summarization system exhibits a high-performance gain in low-resource conditions where the number of training examples is highly limited.https://www.mdpi.com/2076-3417/13/17/9771abstractive summarizationdialogue summarizationkeyword extraction |
| spellingShingle | Chongjae Yoo Hwanhee Lee Improving Abstractive Dialogue Summarization Using Keyword Extraction Applied Sciences abstractive summarization dialogue summarization keyword extraction |
| title | Improving Abstractive Dialogue Summarization Using Keyword Extraction |
| title_full | Improving Abstractive Dialogue Summarization Using Keyword Extraction |
| title_fullStr | Improving Abstractive Dialogue Summarization Using Keyword Extraction |
| title_full_unstemmed | Improving Abstractive Dialogue Summarization Using Keyword Extraction |
| title_short | Improving Abstractive Dialogue Summarization Using Keyword Extraction |
| title_sort | improving abstractive dialogue summarization using keyword extraction |
| topic | abstractive summarization dialogue summarization keyword extraction |
| url | https://www.mdpi.com/2076-3417/13/17/9771 |
| work_keys_str_mv | AT chongjaeyoo improvingabstractivedialoguesummarizationusingkeywordextraction AT hwanheelee improvingabstractivedialoguesummarizationusingkeywordextraction |