1. Contributors Jeremy Brown, Bryan Winters, and Austin Ray
The Automatic Summarization
of Text Documents
By Team [TL;DR]
Contributors
Jeremy Brown, Bryan Winters, and Austin Ray

2. Abstract Description of Problem Statement
When online, users
Can be overloaded by what they see every day
May not have enough time to fully engage the content they encounter
Can cause users to settle for less when they should be getting the best benefit they can

3. Proposed Solution The automatic summarization of bodies of text.
Users can get the same information without having to read a full article, web post, etc.
Automatic Summarization
Generating a summary of text using an algorithm that reduces the total number of sentences.
Retains main points and readability.

4. Our Plan Individual Creation of Three Summarization Algorithms
Based off of work done by others
Language agnostic
Requires Preprocessor
Evaluation of Algorithms
Speed
Efficiency
Readability

5. TextRank How Jeremy’s Implementation Works
Based off of work done by Rada Mihalcea and Paul Tarau
How Jeremy’s Implementation Works
Original text file is ran through the preprocessor, breaking up the text by sentences
Computes sentence similarity using a similarity equation
Grades each sentence based on this similarity
Gets a user given number of sentences to return
Outputs a text file of the top graded sentences in the order they appear in the original text

6. TextRank’s Similarity Equation

7. SumBasic
Based on the work of Nenkova and Vanderwende

8. SMMRY
Based off of the smmry algorithm developed in 2009
How SMMRY Works
Original text file is put through the preprocessor, breaking up the text by sentences
Assigns tokens (points) to each word
Counts and sorts frequency of words based off of total tokens
Reorders sentences based on total tokens per sentence
Outputs a text file of the top sentences. Number of sentences is determined by the user.

9. SMMRY Graphs

10. Running our Algorithms
Ran each algorithm to summarize five texts
Cinderella by the Grimm Brothers
Originally 156 sentences
Summaries: 50 sentences and 25 sentences
Gettysburg Address by Abraham Lincoln
Originally 10 sentences
Summaries: 5 sentences and 2 sentences
Eisenhower’s Farewell Address
Originally 84 sentences
Summaries: 25 sentences and 10 sentences
General MacArthur’s ‘Duty, Honor, Country’
Yellow Submarine by The Beatles
Originally 36 sentences
Summaries: 12 sentences and 6 sentences

11. Comparing Algorithm Results - Speed

12. Comparing Algorithm Results - Efficiency (CPU)

13. Comparing Algorithm Results - Efficiency (Memory)

14. Readability Rubric 4 3 2 1 Purpose Statement Clear
Purpose statement is clearly defined within the summary.
The purpose statement is there but is not fully defined.
The purpose statement can be found it bits and pieces.
No Purpose Statement
Starts Well
Clearly starts the summary off in the direction the original author intended.
Less clear, but the preceding sentences help get the summary started.
The start of the paragraph is in the summary somewhere.
The start is at the end, or the summary did not include something to start the text off.
Ends Well
Clearly ends the summary in the direction the original author intended.
Less clear, but the preceding sentences help get the summary ended.
The end of the paragraph is in the summary somewhere.
The end is at the start, or the summary just trailed off without a conclusion.
Sentence Order
Sentences are in a correct order such that reading is fluid and understandable
Majority of sentences are in order such that the reader has little trouble understanding
Majority of sentences are out of order, but the reader can understand the summary with difficulty
Every sentence is out of order presenting an unintelligible summary
Main Points Clear
Reader can determine all the main points of the original document from summary
Reader can determine the majority of main points of the original document from the summary
Reader cannot determine the majority of main points of the original document from the summary
Reader cannot determine any of the main points of the original document form the summary
Tone
Tone is the same as the original document
Tone is changed slightly (i.e. positive to mostly positive)
Tone is changed vastly (i.e. positive tone to mostly negative tone)
Tone is opposite of original document (i.e. positive to negative)
Readability
Rubric

15. Comparing Algorithm Results - Readability

16. Comparing Algorithm Results - Conclusion
SMMRY
Pros: Lowest memory usage, can produce good summaries
Cons: Slowest, highest CPU usage on average, can produce bad summaries
TextRank
Pros: Highest average readability scores, consistent CPU usage,
Cons: Slower than SumBasic, requires more memory than SMMRY
SumBasic
Pros: Consistently the fastest, consistent readability scores
Cons: Highest memory usage, inconsistent CPU usage, average readability scores

17. Future Work TextRank SMMRY
Look into implementing a regular expression for the preprocessor
Figure out ways to decrease RAM usage
Have every sentence cast a vote for every other sentence
Deal with sentence co-occurrence
SMMRY
Prevent conjunction words from being tokenized
Do not allow for summaries being longer than original text
Reduce NLTK library size
Handles other UNICODE encoding other than ASCII
SumBasic
Better dictionary lookups
Associate grammar counterparts and contractions (e.g. city and cities, it’s and it is)
Reduce implementation’s time complexity

18. Question #1
In the TextRank Similarity Equation, what is the purpose of normalizing?

19. To avoid promoting long sentences.
Answer #1
To avoid promoting long sentences.

20. What is the purpose of a preprocessor?
Question #2
What is the purpose of a preprocessor?

21. Answer #2
A preprocessor is needed to separate sentences in order to compare and rank them.

22. What defines a good summary?
Question #3
What defines a good summary?

23. Answer #3
A good summary has a clear purpose statement, starts/ends well, maintains sentence order, clearly states main point, and keeps the tone consistent with the original text.

24. What type of approach do our three algorithms utilize?
Question #4
What type of approach do our three algorithms utilize?

25. Answer #4
Extraction.
The process of extraction is done by selecting a subset of existing words, phrases, or sentences to form a summary

26. Question #5
What is the purpose of squaring the probability of each word in a chosen sentence in the SumBasic algorithm?

27. To provide context sensitivity.
Answer #5
To provide context sensitivity.
i.e. change the notion of “what is most important to include” based on already summarized information

28. Question #6
Why is our project 380?

29. and oh, we also analyzed them ;)
Answer #6
But really…
The class is about the design and analysis of algorithms. Obviously, we designed three algorithms with unique approaches.
and oh, we also analyzed them ;)

30. The End