Summarize the main points about the evaluation of text summarization systems discussed in the following text:

Evaluating text summarization systems is a crucial but challenging task. The most widely used evaluation method is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between system-generated summaries and human reference summaries based on n-gram statistics. ROUGE scores correlate well with human judgments in many cases, but have limitations as they don't consider factors like coherence, semantic accuracy, and fidelity to the original text. Other automatic metrics like BERTScore use pretrained language models to evaluate semantic similarity between system and reference summaries. However, human evaluation is still considered the gold standard, involving assessments of key qualities like coherence, consistency, non-redundancy, focus, and overall quality on a Likert scale. Human evaluation is more reliable but costly and subjective. Efforts are ongoing to develop better automatic evaluation techniques and standardize human evaluation rubrics in order to benchmark progress in summarization while reducing reliance on labor-intensive manual assessment.
Summarize the main points about the evaluation of text summarization systems discussed in the following text:

Evaluating text summarization systems is a crucial but challenging task. The most widely used evaluation method is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures the overlap between system-generated summaries and human reference summaries based on n-gram statistics. ROUGE scores correlate well with human judgments in many cases, but have limitations as they don't consider factors like coherence, semantic accuracy, and fidelity to the original text. Other automatic metrics like BERTScore use pretrained language models to evaluate semantic similarity between system and reference summaries. However, human evaluation is still considered the gold standard, involving assessments of key qualities like coherence, consistency, non-redundancy, focus, and overall quality on a Likert scale. Human evaluation is more reliable but costly and subjective. Efforts are ongoing to develop better automatic evaluation techniques and standardize human evaluation rubrics in order to benchmark progress in summarization while reducing reliance on labor-intensive manual assessment.
user
The main points about evaluating text summarization systems are: 1) ROUGE is the most widely used metric, evaluating n-gram overlap between system and reference summaries, but has limitations in considering coherence and fidelity. 2) Other metrics like BERTScore use language models to assess semantic similarity. 3) Human evaluation involving assessments of coherence, consistency, etc. on a Likert scale is considered the gold standard but is costly and subjective. 4) Research efforts aim to develop better automatic evaluation techniques and standardize human evaluation rubrics to benchmark progress while reducing manual effort.
assistant
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 the main points of this news article for me in a couple of sentences.

Oakland Athletics broadcaster Glen Kuiper apologized after uttering what sounded like a racial slur while describing a trip to the Negro Leagues Baseball Museum.

In a pregame segment on NBC Sports California, before the A’s played the Kansas City Royals on Friday night, Kuiper talked about a trip to the museum with colleague Dallas Braden but seemingly mispronounced the word “negro,” making it sound instead like a slur.

Later in the game, Kuiper apologized on the air without getting into specifics.
Response: Glen Kuiper, who is the Oakland Athletics broadcaster, apologized for saying a racial slur.  Kuiper mispronounced "negro" when describing a recent visit to the Negro Leagues Baseball Museum. 

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.
