Extractive Summarization of Development Emails

- the produced summary
- the short version of the summary
- the length of the summary
- a FeaturesExtractor instance
- a global tagger
Class Summary behavior:
- summUpMail() appends to the same text all the email sentences marked as relevant, and passes the obtained
summary to MailSummaryView
- makeShortSummary() creates a summary considering only sentences whose relevance leaf is at higher levels
of the tree, until reaching 60% of the length of the original message.
The task of class FeaturesExtractor, in a nutshell, is to implement the algorithm outputted by Weka Explorer.
FeaturesExtractor took as input a Mail object and a tagger, removed the lines of the format “On ,
wrote:" right before a replying-email and the replying-lines starting with ">", and stored:
- the subject of the email
- the passed tagger which will be used to individuate the several grammatical parts of speech
- a sentence-id hash map
- a matrix where, for each sentence of the email, all the values of the extracted relevant features are inserted
Dealing with parts of speech
As we saw at Section 3.5, among the important features for the determination of the relevance of a sentence there is
also num_verbs_norm. To understand how many verbs there are in a sentence, we used the function tagString(<
st ring >) of the MaxentTagger we stored. This tool is a part-of-speech tagger suitable for Java programming,
developed by Stanford natural Language Processing Group 21 . We installed it in Eclipse very easily by importing
stanford-postagger.jar in the Referenced Libraries, and by creating a folder tagger where to place the English
vocabulary files.
Implementation of the relevance tree
The function determineRelevance() computes through a long i f − chain the relevance of a sentence by accessing
the information stored in the features matrix. The result (1 if the sentence is calculated to be “relevant", 0 otherwise)
is then stored back in the last column of the matrix itself.
How to
Using such a plug-in is really intuitive. The user:
1. opens, in Eclipse editor, REmail and REmail Summary views
2. lets CouchDB start (if a shared server among developers does not already exist)
3. clicks on some class to visualize the related emails in the REmail box view
4. reads the extractive summary in REmail Summary box view.
5. If the user judges that the produced summary is still too long, we provide a “Shorten" button that returns a
skimmed summary which is long at most 60% of the original text. We remind, however, that this is a completely
deterministic process, surely not aimed at preserving the human generated-likeness. This functionality is active
only for emails longer than 4 sentences.
21 http://nlp.stanford.edu/software/tagger.shtml
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