🌴 Text Summarization Prompts

Abstract

This section covers the prompts for "Text Summarization" task.

Text summarization is a natural language processing task which involves creating a concise summary of the given text, while still capturing all the important information. In simple words, text summarization aims to generate a concise and informative summary of the given text.

Text summarization can be extractive or abstractive. Extractive summarization identifies important sentences from the original text and then generates the summary by combining those important sentences. Abstractive summarization uses natural language processing techniques to understand the meaning of the given text and then generates the summary in its own words that captures the essential information from the original text.

In simple words, extractive summarization generates a summary by identifying and combining the important sentences from the original text while abstractive summarization understands the original text and generates the summary in its own words. Abstractive summary is more effective compared to extractive summary as abstractive summary is generated in own words rather than selecting the important sentences.

Assume that you want to generate abstractive summary of a research paper abstract. For this, you can write a prompt like this

Prompt

You are an expert AI researcher. Generate an abstractive summary of the given research paper abstract. 

Paragraph: Large language models (LLMs) have been applied in various applications due to their astonishing 
capabilities. With advancements in technologies such as chain-of-thought (CoT) prompting and in-context 
learning (ICL), the prompts fed to LLMs are becoming increasingly lengthy, even exceeding tens of thousands 
of tokens. To accelerate model inference and reduce cost, this paper presents LLMLingua, a coarse-to-fine 
prompt compression method that involves a budget controller to maintain semantic integrity under high 
compression ratios, a token-level iterative compression algorithm to better model the interdependence 
between compressed contents, and an instruction tuning based method for distribution alignment between 
language models. We conduct experiments and analysis over four datasets from different scenarios, i.e., GSM8K, 
BBH, ShareGPT, and Arxiv-March23; showing that the proposed approach yields state-of-the-art performance 
and allows for up to 20x compression with little performance loss.

Constraints: Please start the summary with the delimiter “Abstractive Summary” and limit the number of sentences 
in the abstractive summary to a maximum of three.

Output

Abstractive Summary: Large language models (LLMs) are increasingly being used in various applications due 
to their powerful capabilities. This paper presents LLMLingua, a new prompt compression method that can reduce 
the length of prompts fed to LLMs by up to 20 times with minimal impact on performance. LLMLingua employs a 
novel combination of techniques, including a budget controller, a token-level iterative compression algorithm, 
and instruction tuning, to achieve state-of-the-art compression results.