What programming language you start with really all depends on where you want to go with programming/coding. The great thing about this field is that there are an absolute abundance of smaller fields that you can go into, all using programming in their own unique ways. For web applications, a good start would be with HTML and later moving your way through CSS, JavaScript, JQuery, PHP, SQL, and any of the JavaScript libraries. Ruby is also a popular choice, so I would recommend checking that out too. For more scientific fields or areas with more machine learning and A.I., Python is generally a great place to start as it is widely used in that field of study. C++ is also a very useful language to know for that, but it can be a little more challenging for beginners. For game and application design, languages such as C#, C, Swift, Kotlin, and Java are most often used for that.
Description
Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. A number of algorithms have been developed, with the simplest being one that parses the text, finds the most unique (or important) words, and then finds a sentence or two that contains the most number of the most important words discovered. This is sometimes called "extraction-based summarization" because you are extracting a sentence that conveys the summary of the text.
For your challenge, you should write an implementation of a text summarizer that can take a block of text (e.g. a paragraph) and emit a one or two sentence summarization of it. You can use a stop word list (words that appear in English that don't add any value) from here.
You may want to review this brief overview of the algorithms and approaches in text summarization from Fast Forward labs.
This is essentially what the autotldr bot does.
Example Input
Here's a paragraph that we want to summarize:
The purpose of this paper is to extend existing research on entrepreneurial team formation under
a competence-based perspective by empirically testing the influence of the sectoral context on
that dynamics. We use inductive, theory-building design to understand how different sectoral
characteristics moderate the influence of entrepreneurial opportunity recognition on subsequent
entrepreneurial team formation. A sample of 195 founders who teamed up in the nascent phase of
Interned-based and Cleantech sectors is analysed. The results suggest a twofold moderating effect
of the sectoral context. First, a technologically more challenging sector (i.e. Cleantech) demands
technically more skilled entrepreneurs, but at the same time, it requires still fairly
commercially experienced and economically competent individuals. Furthermore, the business context
also appears to exert an important influence on team formation dynamics: data reveals that
individuals are more prone to team up with co-founders possessing complementary know-how when they
are starting a new business venture in Cleantech rather than in the Internet-based sector.
Overall, these results stress how the business context cannot be ignored when analysing
entrepreneurial team formation dynamics by offering interesting insights on the matter to
prospective entrepreneurs and interested policymakers.
Example Output
Here's a simple extraction-based summary of that paragraph, one of a few possible outputs:
Furthermore, the business context also appears to exert an important influence on team
formation dynamics: data reveals that individuals are more prone to team up with co-founders
possessing complementary know-how when they are starting a new business venture in Cleantech
rather than in the Internet-based sector.
Challenge Input
This case describes the establishment of a new Cisco Systems R&D facility in Shanghai, China,
and the great concern that arises when a collaborating R&D site in the United States is closed
down. What will that closure do to relationships between the Shanghai and San Jose business
units? Will they be blamed and accused of replacing the U.S. engineers? How will it affect
other projects? The case also covers aspects of the site's establishment, such as securing an
appropriate building, assembling a workforce, seeking appropriate projects, developing
managers, building teams, evaluating performance, protecting intellectual property, and
managing growth. Suitable for use in organizational behavior, human resource management, and
strategy classes at the MBA and executive education levels, the material dramatizes the
challenges of changing a U.S.-based company into a global competitor.
Solution
in Python
def summarize(input_file, output_num):
with open("stop_words.txt", "r") as open_file:
stop_text = open_file.readlines()
stop_text = [word.strip() for word in stop_text]
with open(input_file, "r") as open_file:
input_text = open_file.readlines()
input_text = break_into_sentences(input_text)
word_values = {}
for sentence in input_text:
words = sentence.split(' ')
for word in words:
word = strip(word)
if not word in stop_text:
if not word in word_values:
word_values[word] = 1
else:
word_values[word] += 1
sentence_values = []
for sentence in input_text:
sentence_value = 0
words = sentence.split(' ')
for word in words:
sentence_value += word_values.setdefault(word, 0)
sentence_values.append(sentence_value)
for ii in range(0, output_num):
highest_val_ind = sentence_values.index(max(sentence_values))
print(input_text[highest_val_ind])
del input_text[highest_val_ind]
del sentence_values[highest_val_ind]
def break_into_sentences(input_text):
with open("acronyms.txt", "r") as open_file:
acronyms = open_file.readlines()
acronyms = [word.strip() for word in acronyms]
input_text = ''.join(input_text).replace('\n','')
all_sentences = []
current_sentence = []
split_text = input_text.split(' ')
for ind, word in enumerate(split_text):
current_sentence.append(word + ' ')
# TODO: needs acronym checking
for acronym in acronyms:
if acronym in word:
pass
if '.' in word or '?' in word or '!' in word:
next_word_cap = False
if ind != len(split_text) - 1:
if split_text[ind+1].capitalize() == split_text[ind+1]:
next_word_cap = True
if next_word_cap:
current_sentence = ''.join(current_sentence)
all_sentences.append(current_sentence)
current_sentence = []
return all_sentences
def strip(word):
return word.strip().strip(',').strip(':').strip('(').strip(')').lower()
if __name__ == "__main__":
summarize("input_file2.txt", 1)
Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. A number of algorithms have been developed, with the simplest being one that parses the text, finds the most unique (or important) words, and then finds a sentence or two that contains the most number of the most important words discovered. This is sometimes called "extraction-based summarization" because you are extracting a sentence that conveys the summary of the text.
For your challenge, you should write an implementation of a text summarizer that can take a block of text (e.g. a paragraph) and emit a one or two sentence summarization of it. You can use a stop word list (words that appear in English that don't add any value) from here.
You may want to review this brief overview of the algorithms and approaches in text summarization from Fast Forward labs.
This is essentially what the autotldr bot does.
Example Input
Here's a paragraph that we want to summarize:
The purpose of this paper is to extend existing research on entrepreneurial team formation under
a competence-based perspective by empirically testing the influence of the sectoral context on
that dynamics. We use inductive, theory-building design to understand how different sectoral
characteristics moderate the influence of entrepreneurial opportunity recognition on subsequent
entrepreneurial team formation. A sample of 195 founders who teamed up in the nascent phase of
Interned-based and Cleantech sectors is analysed. The results suggest a twofold moderating effect
of the sectoral context. First, a technologically more challenging sector (i.e. Cleantech) demands
technically more skilled entrepreneurs, but at the same time, it requires still fairly
commercially experienced and economically competent individuals. Furthermore, the business context
also appears to exert an important influence on team formation dynamics: data reveals that
individuals are more prone to team up with co-founders possessing complementary know-how when they
are starting a new business venture in Cleantech rather than in the Internet-based sector.
Overall, these results stress how the business context cannot be ignored when analysing
entrepreneurial team formation dynamics by offering interesting insights on the matter to
prospective entrepreneurs and interested policymakers.
Example Output
Here's a simple extraction-based summary of that paragraph, one of a few possible outputs:
Furthermore, the business context also appears to exert an important influence on team
formation dynamics: data reveals that individuals are more prone to team up with co-founders
possessing complementary know-how when they are starting a new business venture in Cleantech
rather than in the Internet-based sector.
Challenge Input
This case describes the establishment of a new Cisco Systems R&D facility in Shanghai, China,
and the great concern that arises when a collaborating R&D site in the United States is closed
down. What will that closure do to relationships between the Shanghai and San Jose business
units? Will they be blamed and accused of replacing the U.S. engineers? How will it affect
other projects? The case also covers aspects of the site's establishment, such as securing an
appropriate building, assembling a workforce, seeking appropriate projects, developing
managers, building teams, evaluating performance, protecting intellectual property, and
managing growth. Suitable for use in organizational behavior, human resource management, and
strategy classes at the MBA and executive education levels, the material dramatizes the
challenges of changing a U.S.-based company into a global competitor.
Solution
in Python
def summarize(input_file, output_num):
with open("stop_words.txt", "r") as open_file:
stop_text = open_file.readlines()
stop_text = [word.strip() for word in stop_text]
with open(input_file, "r") as open_file:
input_text = open_file.readlines()
input_text = break_into_sentences(input_text)
word_values = {}
for sentence in input_text:
words = sentence.split(' ')
for word in words:
word = strip(word)
if not word in stop_text:
if not word in word_values:
word_values[word] = 1
else:
word_values[word] += 1
sentence_values = []
for sentence in input_text:
sentence_value = 0
words = sentence.split(' ')
for word in words:
sentence_value += word_values.setdefault(word, 0)
sentence_values.append(sentence_value)
for ii in range(0, output_num):
highest_val_ind = sentence_values.index(max(sentence_values))
print(input_text[highest_val_ind])
del input_text[highest_val_ind]
del sentence_values[highest_val_ind]
def break_into_sentences(input_text):
with open("acronyms.txt", "r") as open_file:
acronyms = open_file.readlines()
acronyms = [word.strip() for word in acronyms]
input_text = ''.join(input_text).replace('\n','')
all_sentences = []
current_sentence = []
split_text = input_text.split(' ')
for ind, word in enumerate(split_text):
current_sentence.append(word + ' ')
# TODO: needs acronym checking
for acronym in acronyms:
if acronym in word:
pass
if '.' in word or '?' in word or '!' in word:
next_word_cap = False
if ind != len(split_text) - 1:
if split_text[ind+1].capitalize() == split_text[ind+1]:
next_word_cap = True
if next_word_cap:
current_sentence = ''.join(current_sentence)
all_sentences.append(current_sentence)
current_sentence = []
return all_sentences
def strip(word):
return word.strip().strip(',').strip(':').strip('(').strip(')').lower()
if __name__ == "__main__":
summarize("input_file2.txt", 1)
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