What are the possible unsupervised deep learning techniques for text summarization?
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Possible unsupervised deep learning techniques for text summarization include methods based on graph structures and Learning Free Integer Programming Summarizer (LFIP-SUM). The graph-based approach utilizes a pre-trained language model like BERT for sentence representation, constructs a semantic relation graph between sentences, and applies algorithms like MMR for redundancy removal, showing effectiveness in unsupervised multi-document summarization. On the other hand, LFIP-SUM is an unsupervised extractive summarization model that formulates an integer programming problem based on pre-trained sentence embeddings, eliminating the need for labeled training data and parameter training. Both techniques offer promising alternatives for text summarization without the reliance on large labeled datasets or extensive computational resources.
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11 Citations | Possible unsupervised deep learning technique for text summarization is the Learning Free Integer Programming Summarizer (LFIP-SUM) model, which utilizes pre-trained sentence embeddings without parameter training. |
Open access•Proceedings Article 18 May 2021 14 Citations | Not addressed in the paper. |
| Possible unsupervised deep learning techniques for text summarization include using pre-trained language models like BERT for sentence representation, constructing semantic relation graphs, and applying algorithms like MMR for redundancy removal. | |
| Possible unsupervised deep learning techniques for text summarization include using pre-trained language models like BERT for sentence representation and constructing semantic relation graphs for summarization. | |
09 Nov 2022 1 Citations | Not addressed in the paper. |
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