Enhanced transformer for length-controlled abstractive summarization based on summary output area

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 phase. This phase determines the specific size of the summary output slot. The proposed model harnesses enhanced T5 and GPT models, seamlessly adapting summaries to designated slots. The computed area of a given slot is employed in both models to generate abstractive summaries tailored to fit the output slot perfectly. Experimental evaluations on the CNN/Daily Mail dataset demonstrate the model's success in performing length-controlled summarization, yielding superior results.