Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Summarize this paragraph to only provide the key details in one sentence.

The Underneath is a 1995 American crime film directed by Steven Soderbergh and starring Peter Gallagher and Alison Elliott. The film is based on the novel Criss Cross by Don Tracy, and is a remake of the original 1949 film adaptation. The plot revolves around many themes common to film noir, including romantic intrigue, a botched robbery, and a surprise ending.
Response: Based on the novel Criss Cross by Don Tracy, the 1995 American crime film The Underneath directed by Steven Soderbergh and starring Peter Gallagher and Alison Elliot involves many themes common to film noir.

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.

Outline the steps involved in evaluating the quality and effectiveness of text summarization systems, including commonly used evaluation metrics and methodologies.
user
Evaluating text summarization systems involves both intrinsic and extrinsic methods. Intrinsic evaluation compares the generated summary to human-written reference summaries using metrics like ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures overlap of n-grams, and BLEU (Bilingual Evaluation Understudy), which measures modified precision. Extrinsic evaluation assesses the usefulness of summaries for downstream tasks like question answering or information retrieval. Human evaluation, where experts rate summaries for quality, coherence, and informativeness, is also commonly used. Methodologies include using standard datasets, conducting user studies, and analyzing task-specific performance metrics.
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