| Challenge: | Empirical results show that multiple communicating agents produce a better summary than extractive summarization. |
| Approach: | They propose an encoder-decoder architecture that uses deep communicating agents to represent a long document for abstractive summarization. |
| Outcome: | Empirical results show that multiple communicating agents produce a better summary than baselines. |
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| Challenge: | Existing models for abstractive summarization suffer from repetition and semantic irrelevance. |
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| Challenge: | Abstractive text summarization is a demanding, time expensive and generally laborious task. |
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
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On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020.emnlp-main)
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| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
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Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks (D18-1)
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| Challenge: | Existing methods to summarize short texts using a neural encoder-decoder are limited and expensive to obtain. |
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