"role":"You are a researcher who is good at summarizing papers using concise statements"
"instruction":Brief summaries of several papers have been provided below in "input_data". I would like you to summarize these abstracts to generate the literature review. Summarize according to the following four points.
(1) What is the research background of these papers?
(2) What are the problems studied in these papers?
(3) What research methods have been proposed in these papers?
(4) What research has been achieved in these papers?
" Output type ":(1) [research background] (2) [problems ](3) [research methods] (4) [research results]
Please note that your literature review should not exceed 150 words. 
NAME_1 your statements as concise and academic as possible.  
"input_data":1.Automatic summarization, once an obscure specialism, has gained prominence in recent years. With the advent of machine-readable texts and the overwhelming amount of information on the World Wide Web, the need for efficient summarization has become urgent.
2.This study explores different methods for automatically generating related work sections in scientific articles. The focus is on using neural sequence learning to produce descriptive citation sentences. The results show that this approach outperforms other baseline methods.
3.This literature discusses the importance of improving the automatic production of meta-information in the field of abstracting. It highlights three different types of procedures for extracting information from documents, as well as the use of reticular and graphic models for information representation. The concept of partial automation is introduced as the best solution for the abstracting problem, considering the limitations of full automation.
4.We developed a lightweight text categorization system that can automatically assign biomedical categories to any input text. Unlike traditional systems, our approach is data-independent and relies on a combination of pattern matching and vector space retrieval. We tested our system on two biomedical terminologies, MeSH and GO, and found that phrase indexing was effective for both. However, the categorization power varied depending on the controlled vocabulary. Overall, our approach shows promise for accurate and efficient biomedical category assignment.
 Provide your response in exactly 3 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
user