I came across an interesting method called “Chain of Density” that promises to generate increasingly concise and information-rich summaries. Let me share what I’ve learned.
Chain of Density is an iterative summarization technique that aims to create progressively denser summaries of a given text. The process involves repeating two key steps multiple times:
- Identify 1-3 informative entities from the original text that are missing from the previous summary.
- Write a new summary of identical length that incorporates all the information from the previous summary plus the newly identified entities.
What makes this method particularly interesting is its focus on maintaining a consistent word count while increasing information density. This constraint forces the summarization process to become more efficient with each iteration, resulting in highly compact yet comprehensive summaries.
This is based on the paper From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting.
Let’s see it’s progressive summaries generated with Claude:
- Missing Entities: Chain of Density; GPT-4; summarization
This article discusses an interesting approach to text summarization called the Chain of Density method. The researchers propose a novel technique that involves creating increasingly dense summaries using GPT-4, a large language model. The study explores the balance between informativeness and readability in summaries, aiming to understand how much information can be effectively packed into a fixed-length text. The authors conduct experiments and evaluations to assess the impact of this densification process on summary quality. The article provides insights into the potential applications and implications of this summarization technique in various fields.
- Missing Entities: entity-centric; human preferences; CNN/DailyMail dataset
This article discusses the Chain of Density (CoD) method, a novel approach to text summarization using GPT-4. The researchers generate increasingly entity-dense summaries while maintaining a fixed length. They explore the trade-off between informativeness and readability, aiming to determine the optimal information density for summaries. The study utilizes the CNN/DailyMail dataset for experiments and evaluates human preferences for different levels of summary density. The authors conduct both human and automatic evaluations to assess the impact of densification on summary quality. The research provides insights into entity-centric summarization and its potential applications in natural language processing.
- Missing Entities: abstractiveness; fusion; lead bias
The Chain of Density (CoD) method, proposed in this article, uses GPT-4 to generate increasingly entity-dense summaries of fixed length. The researchers explore the trade-off between informativeness and readability in summarization, using the CNN/DailyMail dataset. They evaluate human preferences for different density levels and conduct automatic assessments. The study reveals that CoD summaries become more abstractive, exhibit increased fusion, and reduce lead bias as density increases. Human evaluators prefer summaries with higher entity density than vanilla GPT-4 outputs, but slightly lower than human-written references. The research provides insights into entity-centric summarization and its impact on summary quality, contributing to advancements in natural language processing.
- Missing Entities: GPT-4 Likert-scale ratings; 5,000 unannotated summaries; open-source dataset
This article presents the Chain of Density (CoD) method for text summarization using GPT-4. The approach generates increasingly entity-dense summaries of fixed length, exploring the balance between informativeness and readability. Experiments on the CNN/DailyMail dataset reveal that CoD summaries become more abstractive, exhibit increased fusion, and reduce lead bias as density increases. Human evaluators prefer summaries with higher entity density than vanilla GPT-4 outputs. The study includes GPT-4 Likert-scale ratings of summaries across multiple dimensions. The researchers open-source their annotated test set and 5,000 unannotated CoD summaries for further research. This work contributes to entity-centric summarization and provides valuable resources for the natural language processing community.
- Missing Entities: Fleiss’ kappa; 0.112 inter-annotator agreement; domain-specific limitations
This article introduces the Chain of Density (CoD) method for GPT-4-based text summarization, generating increasingly entity-dense summaries of fixed length. Experiments on CNN/DailyMail reveal CoD summaries become more abstractive, exhibit increased fusion, and reduce lead bias. Human evaluators prefer higher entity density than vanilla GPT-4 outputs. The study includes GPT-4 Likert-scale ratings and open-sources an annotated test set and 5,000 unannotated CoD summaries. However, the research reports low inter-annotator agreement (Fleiss’ kappa of 0.112), highlighting the subjectivity in summary evaluation. The authors acknowledge domain-specific limitations, focusing solely on news summarization. This work advances entity-centric summarization and provides valuable resources for natural language processing research, while emphasizing the need for further investigation into cross-domain applicability and evaluation metrics.
I personally like the 3rd summary, 4&5 are too dense.
You can tweak the prompt to your needs. I’ve used the following:
Focus on the attached Article.
You will generate increasingly concise, entity-dense summaries of the above Article. Repeat the following 2 steps 5 times. Step 1. Identify 1-3 informative Entities (";" delimited) from the Article which are missing from the previously 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 story.
- Specific: descriptive yet concise (5 words or fewer).
- Novel: not in the previous summary.
- Faithful: present in the Article.
- Anywhere: located anywhere in the Article.
Length goal: 160-200 words per summary.
Guidelines:
- The first summary should hit the length goal yet highly non-specific, containing little information beyond the entities marked as missing. Use overly verbose language and fillers (e.g., “this article discusses”) to reach the length goal.
- Make every word count: re-write 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 concise 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 the exact same number of words for each summary.
Answer in Markdown:
- Each summary starts with a list of Missing Entities which are in bold.
- Then the summary follows.
- Put two empty lines between the summaries.
- There a total of 5 summaries.
Print it with artifacts for easy copy-pasting.
Kudos to Jamie Edwards for sharing this on twitter:
>> HomeHave you tried 'Chain of Density' prompting? Rarely fails for me https://t.co/ujRiUjx9Ho https://t.co/ZQHXNLH9dD.
— Jamie Edwards (@jmedwards) September 9, 2024