Large Language Models have gained a lot of attention in recent times due to their excellent capabilities. LLMs are capable of everything from question answering and content generation to language translation and textual summarization. Recent developments in automatic summarization are largely attributable to a change in strategy from supervised fine-tuning on labeled datasets to the use of Large Language Models like OpenAI developed GPT-4 with zero-shot prompting. This change enables careful prompting to customize a variety of summary properties, including length, themes, and style, without the need for extra training.
In automatic summarization, deciding how much information to include in a summary is a difficult task. An excellent summary should strike a careful balance between being comprehensive and entity-centric while avoiding overly dense language that might be confusing to readers. In recent research, a team of researchers has conducted a study using the well-known GPT-4 to create summaries with a Chain of Density (CoD) prompt in order to understand the trade-off better.
The main goal of this study was to find a limit by collecting human preferences for a collection of summaries produced by GPT-4 that are progressively more dense. The CoD prompt comprised several steps, and GPT-4 initially generated a summary with a limited number of listed entities. It then incrementally lengthened the summary by including the missing salient items. In comparison to summaries produced by a conventional GPT-4 prompt, these CoD-generated summaries were distinguished by enhanced abstraction, a higher level of fusion, i.e., information integration, and less bias towards the beginning of the source text.
One hundred items from CNN DailyMail were used in human preference research to evaluate the efficacy of CoD-generated summaries. The study’s results showed that GPT-4 summaries generated with the CoD prompt, which were denser than those generated by a vanilla prompt yet drew close to the density of human-written summaries, were preferred by human evaluators. This implies that achieving the ideal balance between informativeness and readability in summary is crucial. The researchers also released 5,000 unannotated CoD summaries in addition to the human preference study, all of which are available to the public on the HuggingFace website.
The team has summarized their key contributions as follows –
- The Chain of Density (CoD) method has been introduced, which is an iterative prompt-based strategy that gradually improves the entity density of summaries produced by GPT-4.
- Comprehensive Evaluation: The research thoroughly evaluates ever-denser CoD summaries, including manual and automatic evaluations. By favoring fewer entities, clarity, and informativeness in summarizations, this evaluation seeks to understand the delicate balance between the two.
- Open Source Resources: The study offers open-source access to 5,000 unannotated CoD summaries, annotations, and summaries produced by GPT-4. These tools are made available for analysis, assessment, or instruction, promoting continued development in the automatic summarization sector.
In conclusion, this research highlights the ideal balance between compactness and informativeness in automatic summaries, as determined by human preferences, and contends that it is desirable for automated summarization processes to achieve a level of density close to that of human-generated summaries.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.