Abstract

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 English 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.

Highlights

  • Great progress has been made in recent years on abstractive summarization of text documents

  • We propose Multi-level Summarizer (MLS), a supervised approach to generate abstractive summaries of a text document at controllable lengths

  • We have proposed MLS, a supervised approach to construct abstractive summaries at controllable lengths

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Summary

Introduction

Great progress has been made in recent years on abstractive summarization of text documents. Being able to constrain the length of a summary while preserving its desirable properties has many real-world applications One such application is content optimization for variable screen-sizes. Constructing a large enough corpus with summaries budgeted at b, ∀b ∈ (0, 1) may not be possible and/or cost-efficient for a number of domains This is one of the main reasons why most existing works on abstractive summarization evaluate their model on large-scale news corpus datasets (Nallapati et al, 2016; Hermann et al, 2015), leaving out a number of important but low-resource domains (Magooda and Litman, 2020; Parida and Motlicek, 2019) where the number of available training documents is limited. We formalize the summarization task addressed in this paper as follows

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