Context length for text summarization models

This is a question regarding the text summarization approach mentioned in the videos of Week 2 of the NLP with Attention course.

It seems that the decoder-only architecture is used to perform text summarization with the summary of each article simply concatenated to the bottom of the article itself. However, if we want the summary to be able to reference the rest of the article, wouldn’t the max sequence length of the model be as long as the article itself (so that while generating a summary, attention can be given to words in the original text)?

Is this not very inefficient? Would it not be better to pass the original article through an encoder and have a decoder generate the summary, more like a machine translation task?

Hi @gursi26

The utilization of a decoder-only architecture for text summarization often involves concatenating the summary to the end of the original article. While this method enables references from the summary to the source text, it does present challenges. For instance, the model might struggle to effectively capture distant dependencies when the summary needs to refer to words at the start of the article. Additionally, the potential lengthening of the model’s sequence due to concatenation could lead to efficiency concerns.

On the other hand, employing an encoder-decoder architecture can address the limitations encountered with the decoder-only approach. In this setup, the original article undergoes encoding to produce a fixed-length representation, often referred to as a “context vector.” This vector encapsulates the core content of the article. Subsequently, the decoder employs this context vector as input to generate the summary. This architecture parallels machine translation, where an encoder processes the source language and a decoder produces the target language.

The encoder-decoder approach offers several advantages. It proves more efficient due to the fixed input length for the decoder, enhancing training and usage efficiency. Furthermore, the context vector from the encoder adeptly captures the entirety of the article’s content, thus providing a robust resource for the decoder. This proves especially beneficial for lengthy articles or tasks requiring a comprehensive grasp of the input content.

In conclusion, while the decoder-only approach with concatenated text suffices for shorter summaries referencing the source text, adopting an encoder-decoder architecture introduces greater versatility and efficiency, particularly for tasks involving longer inputs or demanding an in-depth understanding of the input material. The choice between the two methods hinges on factors such as task nature, input length, and desired performance levels.

i hope this help

best regards