Summarizing a document within an allocated budget while maintaining its major concepts is a challenging task . If the budget can take any arbitrary value and not known beforehand , it becomes even more difficult. Most of the existing methods for abstractive summarization, including state-of-the-art neural networks are data intensive. If the number of available training samples becomes limited, they fail to construct high-quality summaries .  We propose MLS, an end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets. MLS employs a pair of supervised sequence-to-sequence networks. The first network called theMFS-Net constructs a minimal feasible summary by identifying the key concepts of the input document . The second network called the Pointer-Magnifier then generates the final summary from the minimal feasible summary by leveraging an interpretable multi-headed attention model. Experiments on two cross-domain datasets show that MLS outperforms baseline methods over a range of success metrics including ROUGE and METEOR. We observed an improvement of approximately 4\% in both metrics over the state-of-art convolutional network at lower budgets. Results from a human evaluation study also establish the effectiveness of MLS in generating complete coherent summaries at arbitrary compression budgets .
Abstractive summarization at controllable lengths is a challenging task in natural language processing. It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is not known beforehand . At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical. We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths. The key enabler of our method is an interpretable multi-headed attention mechanism that computes attention distribution over an input document using an array of timestep independent semantic kernels. Each kernel optimizes a human-interpretable syntactic or semantic property. Exhaustive experiments on two low-resource datasets in the English language show that MLS outperforms strong baselines by up to 14.70\% in the METEOR score. Human evaluation of the summaries also suggests that they capture the key concepts of the document at various length-budgets .
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Summarizing a document within an allocated budget while maintaining its major concepts
Abstractive summarization at controllable lengths
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. If the budget can take any arbitrary value and
in natural language processing. It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is
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, it becomes even more difficult. Most of the existing methods for abstractive summarization, including state-of-the-art neural networks are data intensive. If the number of available training samples becomes limited, they fail to construct high-quality summaries
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At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical.
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MLS,
Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths. The key enabler of our method is
D
end-to-end framework to generate abstractive summaries with limited training data at arbitrary compression budgets. MLS employs a pair of supervised sequence-to-sequence networks. The first network called the
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MFS-Net
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constructs a minimal feasible summary by identifying the key concepts of the input document . The second network called the Pointer-Magnifier then generates the final summary from the minimal feasible summary by leveraging an
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model. Experiments on two cross-domain datasets
mechanism that computes attention distribution over an input document using an array of timestep independent semantic kernels. Each kernel optimizes a human-interpretable syntactic or semantic property. Exhaustive experiments on two low-resource datasets in the English language
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baseline methods over a range of success metrics including ROUGE and METEOR. We observed an improvement of approximately 4\% in both metrics over the state-of-art convolutional network at lower budgets. Results from a human evaluation study also establish the effectiveness of MLS in generating complete coherent summaries at arbitrary compression budgets
strong baselines by up to 14.70\% in the METEOR score. Human evaluation of the summaries also suggests that they capture the key concepts of the document at various length-budgets