Enhanced transformer for length-controlled abstractive summarization based on summary output area
Yusuf Sunusi, Nazlia Omar, Lailatul Qadri Zakaria
TL;DR
This paper introduces a new method for generating summaries of specific lengths by using an image processing phase to determine the summary size.
Contribution
The novel approach integrates image processing to control summary length, improving relevance within given constraints.
Findings
The model successfully adapts summaries to fit specific output slot sizes.
Experiments on the CNN/Daily Mail dataset show superior performance in length-controlled summarization.
The method outperforms prior techniques that rely on predefined lengths.
Abstract
Recent advancements in abstractive summarization models, particularly those built on encoder-decoder architectures, typically produce a single summary for each source text. Controlling the length of summaries is crucial for practical applications, such as crafting cover summaries for newspapers or magazines with varying slot sizes. Current research in length-controllable abstractive summarization employs techniques like length embeddings in the decoder module or a word-level extractive module in the encoder-decoder model. However, these approaches, while effective in determining when to halt decoding, fall short in selecting relevant information to include within the specified length constraint. This article diverges from prior models reliant on predefined lengths. Instead, it introduces a novel approach to length-controllable abstractive summarization by integrating an image processing…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications