文本概括 Summarizing
作者 吴恩达教授
1 引言
当今世界上有太多的文本信息，几乎没有人能够拥有足够的时间去阅读所有我们想了解的东西。但令人感到欣喜的是，目前LLM在文本概括任务上展现了强大的水准，也已经有不少团队将这项功能插入了自己的软件应用中。
本章节将介绍如何使用编程的方式，调用API接口来实现“文本概括”功能。
首先，我们需要OpenAI包，加载API密钥，定义getCompletion函数。
import openai
import os
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
openai.api_key = OPENAI_API_KEY
def get_completion(prompt, model="gpt-3.5-turbo"):
messages = [{"role": "user", "content": prompt}]
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=0, # 值越低则输出文本随机性越低
)
return response.choices[0].message["content"]2 单一文本概括Prompt实验
这里我们举了个商品评论的例子。对于电商平台来说，网站上往往存在着海量的商品评论，这些评论反映了所有客户的想法。如果我们拥有一个工具去概括这些海量、冗长的评论，便能够快速地浏览更多评论，洞悉客户的偏好，从而指导平台与商家提供更优质的服务。
输入文本
prod_review = """
Got this panda plush toy for my daughter's birthday, \
who loves it and takes it everywhere. It's soft and \
super cute, and its face has a friendly look. It's \
a bit small for what I paid though. I think there \
might be other options that are bigger for the \
same price. It arrived a day earlier than expected, \
so I got to play with it myself before I gave it \
to her.
"""输入文本（中文翻译）
prod_review_zh = """
这个熊猫公仔是我给女儿的生日礼物，她很喜欢，去哪都带着。
公仔很软，超级可爱，面部表情也很和善。但是相比于价钱来说，
它有点小，我感觉在别的地方用同样的价钱能买到更大的。
快递比预期提前了一天到货，所以在送给女儿之前，我自己玩了会。
"""2.1 限制输出文本长度
我们尝试限制文本长度为最多30词。
prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site.
Summarize the review below, delimited by triple
backticks, in at most 30 words.
Review: ```{prod_review}```
"""
response = get_completion(prompt)
print(response)中文翻译版本
prompt = f"""
你的任务是从电子商务网站上生成一个产品评论的简短摘要。
请对三个反引号之间的评论文本进行概括，最多30个词汇。
评论: ```{prod_review_zh}```
"""
response = get_completion(prompt)
print(response)2.2 关键角度侧重
有时，针对不同的业务，我们对文本的侧重会有所不同。例如对于商品评论文本，物流会更关心运输时效，商家更加关心价格与商品质量，平台更关心整体服务体验。
我们可以通过增加Prompt提示，来体现对于某个特定角度的侧重。
侧重于运输
prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give feedback to the \
Shipping deparmtment.
Summarize the review below, delimited by triple
backticks, in at most 30 words, and focusing on any aspects \
that mention shipping and delivery of the product.
Review: ```{prod_review}```
"""
response = get_completion(prompt)
print(response)中文翻译版本
prompt = f"""
你的任务是从电子商务网站上生成一个产品评论的简短摘要。
请对三个反引号之间的评论文本进行概括，最多30个词汇，并且聚焦在产品运输上。
评论: ```{prod_review_zh}```
"""
response = get_completion(prompt)
print(response)可以看到，输出结果以“快递提前一天到货”开头，体现了对于快递效率的侧重。
侧重于价格与质量
prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site to give feedback to the \
pricing deparmtment, responsible for determining the \
price of the product.
Summarize the review below, delimited by triple
backticks, in at most 30 words, and focusing on any aspects \
that are relevant to the price and perceived value.
Review: ```{prod_review}```
"""
response = get_completion(prompt)
print(response)中文翻译版本
prompt = f"""
你的任务是从电子商务网站上生成一个产品评论的简短摘要。
请对三个反引号之间的评论文本进行概括，最多30个词汇，并且聚焦在产品价格和质量上。
评论: ```{prod_review_zh}```
"""
response = get_completion(prompt)
print(response)可以看到，输出结果以“质量好、价格小贵、尺寸小”开头，体现了对于产品价格与质量的侧重。
2.3 关键信息提取
在2.2节中，虽然我们通过添加关键角度侧重的Prompt，使得文本摘要更侧重于某一特定方面，但是可以发现，结果中也会保留一些其他信息，如价格与质量角度的概括中仍保留了“快递提前到货”的信息。有时这些信息是有帮助的，但如果我们只想要提取某一角度的信息，并过滤掉其他所有信息，则可以要求LLM进行“文本提取(Extract)”而非“文本概括(Summarize)”。
prompt = f"""
Your task is to extract relevant information from \
a product review from an ecommerce site to give \
feedback to the Shipping department.
From the review below, delimited by triple quotes \
extract the information relevant to shipping and \
delivery. Limit to 30 words.
Review: ```{prod_review}```
"""
response = get_completion(prompt)
print(response)中文翻译版本
[19]:
prompt = f"""
你的任务是从电子商务网站上的产品评论中提取相关信息。
请从以下三个反引号之间的评论文本中提取产品运输相关的信息，最多30个词汇。
评论: ```{prod_review_zh}```
"""
response = get_completion(prompt)
print(response)
3 多条文本概括Prompt实验
在实际的工作流中，我们往往有许许多多的评论文本，以下展示了一个基于for循环调用“文本概括”工具并依次打印的示例。当然，在实际生产中，对于上百万甚至上千万的评论文本，使用for循环也是不现实的，可能需要考虑整合评论、分布式等方法提升运算效率。
review_1 = prod_review
# review for a standing lamp
review_2 = """
Needed a nice lamp for my bedroom, and this one \
had additional storage and not too high of a price \
point. Got it fast - arrived in 2 days. The string \
to the lamp broke during the transit and the company \
happily sent over a new one. Came within a few days \
as well. It was easy to put together. Then I had a \
missing part, so I contacted their support and they \
very quickly got me the missing piece! Seems to me \
to be a great company that cares about their customers \
and products.
"""
# review for an electric toothbrush
review_3 = """
My dental hygienist recommended an electric toothbrush, \
which is why I got this. The battery life seems to be \
pretty impressive so far. After initial charging and \
leaving the charger plugged in for the first week to \
condition the battery, I've unplugged the charger and \
been using it for twice daily brushing for the last \
3 weeks all on the same charge. But the toothbrush head \
is too small. I’ve seen baby toothbrushes bigger than \
this one. I wish the head was bigger with different \
length bristles to get between teeth better because \
this one doesn’t. Overall if you can get this one \
around the $50 mark, it's a good deal. The manufactuer's \
replacements heads are pretty expensive, but you can \
get generic ones that're more reasonably priced. This \
toothbrush makes me feel like I've been to the dentist \
every day. My teeth feel sparkly clean!
"""
# review for a blender
review_4 = """
So, they still had the 17 piece system on seasonal \
sale for around $49 in the month of November, about \
half off, but for some reason (call it price gouging) \
around the second week of December the prices all went \
up to about anywhere from between $70-$89 for the same \
system. And the 11 piece system went up around $10 or \
so in price also from the earlier sale price of $29. \
So it looks okay, but if you look at the base, the part \
where the blade locks into place doesn’t look as good \
as in previous editions from a few years ago, but I \
plan to be very gentle with it (example, I crush \
very hard items like beans, ice, rice, etc. in the \
blender first then pulverize them in the serving size \
I want in the blender then switch to the whipping \
blade for a finer flour, and use the cross cutting blade \
first when making smoothies, then use the flat blade \
if I need them finer/less pulpy). Special tip when making \
smoothies, finely cut and freeze the fruits and \
vegetables (if using spinach-lightly stew soften the \
spinach then freeze until ready for use-and if making \
sorbet, use a small to medium sized food processor) \
that you plan to use that way you can avoid adding so \
much ice if at all-when making your smoothie. \
After about a year, the motor was making a funny noise. \
I called customer service but the warranty expired \
already, so I had to buy another one. FYI: The overall \
quality has gone done in these types of products, so \
they are kind of counting on brand recognition and \
consumer loyalty to maintain sales. Got it in about \
two days.
"""
reviews = [review_1, review_2, review_3, review_4]for i in range(len(reviews)):
prompt = f"""
Your task is to generate a short summary of a product \
review from an ecommerce site.
Summarize the review below, delimited by triple \
backticks in at most 20 words.
Review: ```{reviews[i]}```
"""
response = get_completion(prompt)
print(i, response, "\n")