Imagine getting summaries from AI that aren't just compressed text, but truly useful summaries that accurately convey the essence, preserve all key facts, and remain concise! 🚀 This is actually possible thanks to an approach inspired by Direct Preference Optimization, and today I'll show you how to apply it in practice.What is this approach and why does it work? 🤔

The idea is to make AI look at summarization through the lens of three key quality dimensions:

1. Faithfulness — accurate representation of the source text without distortions 📝

2. Completeness — inclusion of all important facts and details 🔍

3. Conciseness — absence of unnecessary words and repetitions ✂️

Research shows that this multi-dimensional evaluation with specific criteria significantly improves summarization quality!

Here's the prompt I now use every day:

Summarize this text in three stages:
1. First, list 5 key facts from the text
2. Create a brief summary based on these facts
3. Check your summary against three parameters:
   - Faithfulness: how accurately is the information conveyed? [✓/✗]
   - Completeness: are all key facts included? [✓/✗]
   - Conciseness: are there any unnecessary words or repetitions? [✓/✗]
4. If any parameter is marked ✗, fix your summary and present the final version

Text: [your text here]

Once you've created the perfect summary using this method, why stop there? You can take it to the next level by converting it to speech using AI voice generators like speechgen.io. Well-crafted summaries are ideal for audio conversion because:

• They're already optimized for clarity and brevity

• The multi-dimensional approach ensures they flow naturally when spoken

• Concise summaries make for perfect 2-3 minute audio clips

This combination is incredibly powerful for busy professionals who prefer to listen rather than read, for creating accessible content, or for quickly sharing insights with your team through audio messages. I've found that hearing my summaries read aloud also helps me catch any remaining awkward phrasing!

1. Multi-stage process forces AI to slow down and process information more thoroughly, instead of immediately providing a superficial generalization

2. Identifying key facts before writing the summary solves the main problem — omission of important information. When AI first determines what's important and then writes the summary, the result is much more complete

3. Self-evaluation across three dimensions mimics multi-dimensional feedback, making AI assess its work from different angles

4. Improvement cycle — forces AI not to stop at the first version, but to iteratively improve the result

• At work: Time spent reviewing reports reduced by 40%, while I'm better at capturing key points

• In education: Notes became more informative and compact simultaneously

• When analyzing news: I get a more balanced picture without missing important details

• For long academic texts: Add "Structure the summary by sections: Purpose, Methods, Results, Conclusions"

• For news articles: Add "Highlight the main event, its causes, and possible consequences"

• For business documents: Add "Emphasize actions requiring attention and critical deadlines"

I've been using this approach for a month and it's seriously saving me time processing information. At the same time, the quality of my decisions and depth of understanding materials have only increased!

How do you usually work with summarization? Try this method and share your results in the comments!

#gpt #ai