As you know extractive text summarization is a binary classification problem!(a sentence should be included in summary or not). we have developed our text summarization system with three different algorithms and evaluated them with ROUGE. here is our result
My question is which parametes is more important in text summarization systems. As you can see model 1 has higher recall but lower precision and model 2 has lower recall but higher precision so which one should be selected as the best model?
as you know text summarization is a binary classification problem!(a sentence should be included in summary or not).
This is extractive text summarization.
model 2 has lower recall but higher precision
I assume you meant model 3
which one should be selected as best model?
That depends on the user needs: sometimes it is preferable to have a bit too many sentences that somehow overlap, sometimes it is preferable to have a short summary potentially skipping important sentences.
BLEU
ROUGE
Rouge measures recall
Recall Oriented Understudy for Gisting Evaluation -W(Human Reference Summary) In w(machine generates summary)
That is how much the words (and/or n-grams) in the machine generates summaries appeared in the machine generated summaries.
Overlap of N-grams between the system and references summaries. -Rouge N, ehere N is n-gram
reference_text = """Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". See glossary of artificial intelligence. The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data, including images and videos. Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or "neural networks"), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers). The traditional problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, neural networks and methods based on statistics, probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy and many others. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabatedly. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment. In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science."""
Abstractive summarization
# Abstractive Summarize
len(reference_text.split())
from transformers import pipeline
summarization = pipeline("summarization")
abstractve_summarization = summarization(reference_text)[0]["summary_text"]
Abstractive Output
In computer science AI research is defined as the study of "intelligent agents" Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving" Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go)
Extractive summarization
# Extractive summarize
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lex_rank import LexRankSummarizer
parser = PlaintextParser.from_string(reference_text, Tokenizer("english"))
# parser.document.sentences
summarizer = LexRankSummarizer()
extractve_summarization = summarizer(parser.document,2)
extractve_summarization) = ' '.join([str(s) for s in list(extractve_summarization)])
Extractive Output
'Colloquially, the term "artificial intelligence" is often used to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.'
Using Rouge to Evaluate abstractive Summary
from rouge import Rouge
r = Rouge()
r.get_scores(abstractve_summarization, reference_text)
Using Rouge Abstractive summary output
[{'rouge-1': {'f': 0.22299651364421083,
'p': 0.9696969696969697,
'r': 0.12598425196850394},
'rouge-2': {'f': 0.21328671127225052,
'p': 0.9384615384615385,
'r': 0.1203155818540434},
'rouge-l': {'f': 0.29041095634452996,
'p': 0.9636363636363636,
'r': 0.17096774193548386}}]
Using Rouge to Evaluate abstractive Summary
from rouge import Rouge
r = Rouge()
r.get_scores(extractve_summarization, reference_text)
Using Rouge Extractive summary output
[{'rouge-1': {'f': 0.27860696251962963,
'p': 0.8842105263157894,
'r': 0.16535433070866143},
'rouge-2': {'f': 0.22296172781038814,
'p': 0.7127659574468085,
'r': 0.13214990138067062},
'rouge-l': {'f': 0.354755780824869,
'p': 0.8734177215189873,
'r': 0.22258064516129034}}]
