human
Title: News Summarization and Evaluation in the Era of GPT-3
Summary: We show that humans overwhelmingly prefer GPT-3 summaries, but these also do not suffer from common dataset-specific issues such as poor actuality.

Title: Benchmarking Large Language Models for News Summarization
Summary: We show that instruction tuning, not model size, is the key to the LLM’s zero-shot summarization capability.

Title: On Learning to Summarize with Large Language Models as References
Summary: We propose a novel reward-based learning paradigm for text summarization models that considers the LLMs as the reference or the gold-standard oracle on commonly used summarization datasets such as the CNN/DailyMail dataset.

Title: LMGQS: A Large-scale Dataset for Query-focused Summarization
Summary: We propose a large-scale query-focused summarization dataset for query-centric summarization and demonstrate state-of-the-art performance on multiple existing QFS benchmarks.

Title: Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
Summary: We study the efficacy of large language models as multi-dimensional evaluators using in-context learning, obviating the need for large training datasets.

Give me a overview of the list of papers above.
gpt
--- Reasoning ---
1. Is the question multiple-choice question?
No
2. Is it implied multiple-choice question?
No
3. If two experts were independently asked the question, will the final answer be exactly the same?
No

--- Answer ---
open