Summarization in Computational linguistics

Name: Ahmad Mashhood
Subject: Computational
Linguistics
Assignment Topic
Summarization of single documents technical articles by computer
summarizer tools.
Submitted to: Sir. Aamir Wali
FAST- NU Lahore Campus

Summarization:
Hovey, E. H. (2005) defined summary as a text which is formulated on the
criteria of “Significant portion of information from the original text ” and the
criteria of its length in comparisonwith the original text which should not be more
than the half of the original text.
Mani and Maybury (1999) described the process of“TextSummarization” they
considered it a process like “distillation” in which the most essential and
significant information from one or more text is collected and this information is
presented in an “abridge form”.
Automated Summarization:
Human made summaries generally done using human intelligent
capabilities. The advancement in computer processingsystems and Natural
language processing opened a new domain of research, whose focus was to
producehuman like abstractive summaries of single or multiple document texts.
The use of computer programs, online tools, resulted in “auto abstract” a term
coined by Luhen (1958). He was the first personto give a significant work in the
field of automated summarization.
Needof automated summaries:
The need of automated summaries is based on well-defined
purposes and goals. With the advancement of extensive text on the computer web
pages, document archives, various newspaper articles, reports, based on the same
event it became difficult to read this kind of extensive information by human
beings. It requires time, human intelligence resources, which results in difficulty in
decision making process.If automated summaries are provided in no time
constraints, it will save human resources, effort, and will facilitate decision
making process.
Goalof Automated Summarization research:
The goal of this domain of research of Natural
language processing (NLP) and artificial intelligence is to achieve the generation
of automated abstracts with high human like similarity. Although more work is
needed to move forward from extractive summaries.

Difference betweenabstractand extract summary:
The basic difference between abstract and extract is
(1)In extracts different words or sentences are selected from the original text
and then they are combined together using a chronological sequence of the
original text. Key words are also identified by using extracting technique.
(2) While abstracts are better oriented and sequenced, in which words are
paraphrased or new words are used and they have or should have the ability
to replace the original text. Research is happening to achieve this level.
Types of summaries:
The summaries can be generally classified on the basis of extraction or abstraction
but there are many new kinds of research oriented summaries are produced which
are following, it can be
Outline of a document text, main heading of a news articles, snippets, which
are formed by giving a summary of a web page, when we searchthrough
searchengine.
It can be single document summary or multi document summary.
Generic summaries: These summaries gave us significant information as their
focus is not on the kind or user relevant information.
Query based Summaries: when computer have gave answer to complex questions
it uses the process ofsummarization after information retrieval and gave a user
relevant information based summary. Snippets are also summaries
Single documents summarization of technical articles:
The summaries which are formed by single documents of technical articles are
generally extracts. There are three general steps or problems as quoted by Martin
and Juffrusky. Whenever a computer programme has to summarize a single
document text article.

(1). which part of the original text content should be selected?
The content selectionforsummarization should be at the level of sentences while
summarizing single documents technical article. It is generally assumed before
programming a summarizer tool.
(2). the second problem is related to the arrangement of the extracted sentences.
This ordering of information decides the structures of the summary.
(3). the third problem is to make the arranged sentences fit into the context of the
summary. Which is known as sentence realization.
In order to achieve this stage we have to reject certain portion of sentences while,
certain portion are considerimportant for contextual clarity. Non-significant
phrases are removed, many sentences showing similar words are placed together to
make a coherent summary.
Flow diagram of a generic single document summarizer;
(1).ContentSelection:
It can be of two kinds
(A). Unsupervised content selection:
It is just like classifying sentences using classifier.
Which labels each sentence, with a binary label
(Important vs unimportant) or (extract worthy vs not extract worthy).
Simplest unsupervised algorithm as devised by (Luhen, 1958) it refers towards selection of more
salient or
Information carrying sentences. Can be calculated by frequency method but usual now day’s
salience is calculated by using weighting scheme.
Tf-idf
Weight (wi) = tfij multiply idfi

Supervised content selection:
Classification:
Position:T1, p2S1, P3S1, P4S1, P1S1, P2S2
Cue phrases:in short, in conclusion, in summary. Etc.
Word significance,
Sentence length, shorter one,
Cohesion: lexical chains, more terms for chains is a significant sentence,
Probability:
P (extra worthy(s) |f1, f2, f3…..Fn). > 0.5
Alignment:
Alignment algorithm such as HMMs (Jing, 2002), parallel corporacan also be
used.
Sentence simplification:
It is also known as sentence “ compression” uses algorithm of a parser or partial
parser , which uses rules of elimination purposed byZarjic et al (2007), Corney et
al (2006) and Vander et al (2007 a)
Remove. Appositives, attribution clauses, Abbreviations without named entities,
initial adverbials.
Evaluation of the Summarizers:
Recall: it is the fraction of sentences chosen by human that were identified by the
system correctly;
Recall = | System –human select overlap|
| Sentence selection by human|

Precision: it is the fraction of system sentence which were identified by it
correctly.
F-1 Score= it refers towards harmonic mean of precision and recall
F1 = 2. Precision .recall
Precision + Recall
Automated metrics ROUGE (Recall-oriented Understudy for Gisting Evaluation).
It is much efficient as compare to other methods.
References:
(1). Hovy, E. H. Automated Text Summarization. In R. Mitkov (ed), The Oxford
Handbookof Computational Linguistics, chapter 32, pages 583–598. Oxford
University Press, 2005.
(2). [39] Mani, I., House, D., Klein, G., et al. The TIPSTER SUMMAC Text
Summarization Evaluation. In Proceedings of EACL, 1999.
(3). Luhn, H., P. The Automatic Creation of Literature Abstracts. In Inderjeet Mani
and Mark Marbury, editors, Advances in Automatic Text Summarization. MIT
Press, 1999

Summarization in Computational linguistics

Summarization in Computational linguistics

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