Improve your summarising - ChatGPT prompt

One of the most popular uses of Generative AI is summarising documents and information sources. Now for the but, could it be even better?

Adams et (2023) the issues of using ChatGPT4 to produce the right amount of information in a summary. Balancing the detail in the summary without it becoming too dense, it is hard to follow, whilst ensuring it is relevant to the source. In the paper they investigated applying a technique they called "Chain of Density" (COD) to produce a better summary compared to a standard "summarise is this" prompt.  

Chain of Density in Summary

Start with the standard summarise this kind of prompt - fairly non-specific.

1. Identify one to three more informative entities from the article

2. Write a new denser summary of the same length including all the entities identified (don't remove entities when identified) expanding the detail

Repeat steps 1 and 2 around 5 times. Applying guidelines around improving on the previous summary but must be self-contained (understood without the article),

To try this out I applied it to a paper

M. A. Jameel, T. Kanakis, S. Turner, A. Al-Sherbaz, W. S. Bhaya and M. Al-khafajiy, "An Intelligent Routing Approach for Multimedia Traffic Transmission Over SDN," 2023 15th International Conference on Developments in eSystems Engineering (DeSE), Baghdad & Anbar, Iraq, 2023, pp. 118-124, doi: 10.1109/DeSE58274.2023.10100250.

The prompt (below in bold) used was very slightly modified version from (Adams et al, 2023)

You will generate increasingly concise entity-dense summarises of the article (Article)loaded in. Repeat the following 2 steps 5 times Step 1. Identity 1-3 informative Entities (“;” delimited)from the article which are missing from the previous generated summary. Step 2. Write a new denser summary of identical length which covers every entity and detail from the previous summary plus the Missing Entities. A Missing Entity is:- -Relevant: to the main article. -Specific: descriptive yet concise (5 words or fewer). -Faithful: present in the Article. -Anywhere: located anywhere in the Article. Guidelines: -The first summary should be long (8-10 sentences, ~250 words) yet highly non-specific, containing little information beyond the entities marked as missing. Use overly verbose language and fillers (e,g. “this articles discusses”) to reach ~250 words. -Make every word count: rewrite the previous summary to improve flow and make space for additional entities. -Make space with fusion, compression, and removal of uninformative phrases like “the article discusses”. -The summaries should become highly dense and yet self-contained, e.g. easily understood without the Article. -Missing entities can appear anywhere in the new summary. -Never drop entities from the previous summary. If space cannot be made, add fewer new entities. Remember , use exact same number of words for each summary.

Produces 

Version 1

In an era where multimedia applications, especially video streaming services, are burgeoning due to the swift advancement and proliferation of user devices and internet services, ensuring quality of service (QoS) and quality of experience (QoE) for end-users presents a formidable challenge for service providers and network administrators. The document delves into the complexities surrounding multimedia traffic transmission over networks, highlighting the issues of bandwidth, delay, jitter, and loss ratio that often impede the delivery of high-quality video streaming services. It proposes an innovative solution that marries the principles of reinforcement learning with software-defined networking (SDN) to create a dynamic, intelligent routing framework capable of navigating these challenges. The framework aims to optimize routes for multimedia traffic, particularly video streaming, by learning from network conditions and making informed routing decisions to enhance end-user satisfaction. Simulation results underscore the effectiveness of this approach, demonstrating improvements in viewing quality, throughput, and stability of video quality during streaming, compared to existing techniques. The involvement of various academic institutions and researchers underscores the collaborative effort to address these network challenges, with the ultimate goal of refining multimedia streaming experiences in an ever-evolving digital landscape.

Fifth version

Leveraging the power of SDN and reinforcement learning, this paper advances multimedia traffic routing, focusing sharply on improving video streaming under diverse traffic conditions. It strategically employs machine learning for insightful analytics in network optimization, guiding real-time decisions to mitigate common QoS challenges. This intelligent routing framework ensures robust network performance, enhancing video delivery mechanisms to provide consistent, quality streams. By dynamically adapting routes to optimize for bandwidth and latency, it sets a benchmark in multimedia service provision, prioritizing user QoE. Simulation evidence underscores its efficiency, highlighting its superiority in ensuring high-quality video streaming experiences against traditional routing methods. This approach not only demonstrates the practical application of analytics and machine learning in network management but also marks a significant stride towards achieving unparalleled consistency and quality in multimedia streaming services, even amidst fluctuating network traffic.

Comment.

Both are fine, but personally I believe the final is a more informative read. But have a go yourself cut and paste the prompt in bold and put an article through it. If you are using the free version of ChatGPT or Gemini you might change the first line to say that the Article is going to be entered in the prompt that follows and then enter the text in the following prompt. Have fun and please share your views in the comments.

Reference

Griffin Adams, Alexander Fabbri, Faisal Ladhak, Eric Lehman, Noémie Elhadad

From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting

arXiv:2309.04269v1 (Preprint)

https://doi.org/10.48550/arXiv.2309.04269

All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruonn