people

Even in the case where a large number of schools have not established evaluation indicators suitable for job characteristics, subject characteristics, and research nature.

Things like the number of SCI papers and paper impact factors have even become hard currency in academic circles.

At least at this time in academic circles (domestic) SCI is quite valuable.

When it comes to future development, although Lin Hui cannot be said to be obsessed with academics.

In the next few years, major universities abandoned the “thesis-only theory”.

It is required that evaluation paths with different emphasis should be established for different types of scientific research work.

The evaluation focuses on the innovation level and scientific value of the paper, and does not use the relevant indicators of SCI papers as a direct basis for judgment;

The focus of technical evaluation on solving key technical problems in production practice should focus on actual contributions (for example, the actual effects of new technologies, new products, and new processes brought to industrial application)

The paper should not be used as the sole basis for evaluation.

After that, SCI was not so deified.

Prior to this, SCI had always been on the altar.

But it seems that things are a little different now.

After rebirth, Lin Hui felt that it was just SCI and not that far away.

Not only is it not far away, it is actually very easy.

If the scientific research dogs in the next few years knew about Lin Hui's emotions at this time, he would probably have murderous intentions.

But it is true.

If you have the ability, you can be reborn...

Anyway, Lin Hui felt that the paper compiled by Eve Carly should be published.

There is absolutely nothing wrong with SCI.

Can articles at the level of ordinary journals in the previous life become SCI in this time and space?

It seems a bit unbelievable, but it is true.

The progress of the times is rapid.

Lin Hui read the paper compiled by Eve Carly.

Although the technology inside is nothing to Lin Hui.

But there are too many novel and groundbreaking ideas for people in this time and space.

Lin Hui thinks there is absolutely no problem with publishing such an article to SCI.

Even SCI District 1 is not a big problem.

And with the help of some academic channels at MIT.

It is not impossible for a published paper to be directly listed in a top journal.

Although Eve Carly has compiled the results of recent discussions between Lin Hui and her in the form of a paper, and she has compiled them quite well.

But Lin Hui decided to improve it based on the paper written by Eve Carley.

After all, this was Lin Hui’s first purely academic performance in this time and space.

For this first show, Lin Hui hopes to be perfect.

Although almost nothing in the world is perfect.

But Lin Hui's philosophy has always been to either don't do it or to do it to the extreme.

Driven by this belief, Lin Hui will certainly go all out for his first paper after his rebirth.

Although this is not the first time Lin Hui has written about this aspect of the paper.

But I obviously can't remember this kind of thing.

Although the code is sometimes written irregularly, it may run inexplicably.

But if there are too many loopholes in the paper or something.

That's really ridiculous.

In short, although Lin Hui is eager to produce results in this aspect of the paper.

But when it comes to actually taking action, there is no rush.

It seems that it can only be polished with time.

All in all, this is destined to be a sleepless night.

Originally, Lin Hui didn't need to be so anxious.

However, the email sent by Eve Carly also mentioned the follow-up of generative summary algorithms in the United States.

Although the situation is not pessimistic, it cannot be said to be very optimistic.

After Lin Hui tinkered with the generative summary algorithm.

Many commercial scientific research institutions in the United States are rapidly following Lin Hui's research.

In addition, many American universities with strong computer capabilities (including but not limited to MIT, Stanford University, Carnegie Mellon University, etc.) are also following this direction.

It is not surprising that these overseas scientific research institutions would quickly follow up on Lin Hui’s research.

Although research in this area involving text summarization, a subdivision of natural language processing, seems inconspicuous.

Most of the ordinary people don't even know that anyone is engaged in this field.

But this does not hinder the importance of text summarization for the progress of human civilization.

Lin Hui has conducted many demonstrations in this regard before.

In fact, these overseas scientific research teams in this time and space should attach great importance to the research of text summarization from the beginning.

It's just that now the level of emphasis has been raised to another level.

The reason for raising the level of attention to a higher level is inextricably linked to the commotion caused by Lin Hui.

After Lin Hui’s research results appeared.

Currently, the automatic text summarization technologies commonly used at home and abroad can be divided into two types according to different methods of summarization generation:

Extractive text summarization and generative text summarization.

The method of extractive text summarization is simple to implement, just extract existing sentences from the document to form a summary.

Generative text summarization uses natural language understanding technology to perform grammatical and semantic analysis of text and fuse information, and generate new summary sentences based on this.

Since Lin Hui just came up with the generative summary algorithm not long ago.

Therefore, except for the application of the generative summary algorithm on Nanfeng APP, its application scope is not too wide.

On the contrary, the extractive method is more widely used due to some historical evolution.

But this does not negate the value of generative text summarization.

The academic level has never been dominated by the majority.

The truth is often in the hands of a few people.

In the final analysis, extractive text summarization can only be regarded as a combinatorial optimization problem.

This is ultimately inferior to generative text summarization.

Although it seems inappropriate to forcibly divide two methods of dealing with the same problem (text summary) into superior and inferior ones.

But what is the purpose of human beings in text summarization?

What is the purpose of human research in natural language processing?

After all, it is just to better understand natural language and process natural language more efficiently.

Measured from this perspective, the ability of generative summary algorithms in understanding natural language is undoubtedly higher than that of extractive summary algorithms.

Therefore, it is not biased to call the generative text summarization algorithm superior and the extractive text summarization algorithm inferior.

I think these overseas research teams must have seen that the generative summary algorithm developed by Lin Hui has improved the machine's ability to understand natural language to a higher level.

Only then will the emphasis on research on generating abstracts be further enhanced.

I have to say, the direction is right.

In fact, due to the rise of the field of artificial intelligence in the past, generative text summarization based on artificial intelligence has made a qualitative leap. After that, generative text summarization has become the main research direction of generative summarization.

However, just the fact that some scientific research institutions have increased their investment in text summarization algorithms is not enough for Lin Hui to pay so much attention to it.
Chapter completed!