Combining linguistic and machine learning techniques for email summarization

Muresan, Smaranda; Tzoukermann, Evelyne; Klavans, Judith L.

doi:10.3115/1117822.1117837

Cited by 38 publications

(16 citation statements)

References 13 publications

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“…Muresan et al (2001) took the approach of summarizing individual email messages, first using linguistic techniques to extract noun phrases and then employing machine learning methods to label the extracted noun phrases as salient or not. Corston-Oliver et al (2004) focused on identifying speech acts within a given email, with a particular interest in task-related sentences.…”

Section: Email Summarizationmentioning

confidence: 99%

Summarizing spoken and written conversations

Murray

Carenini

2008

Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08

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In this paper we describe research on summarizing conversations in the meetings and emails domains. We introduce a conversation summarization system that works in multiple domains utilizing general conversational features, and compare our results with domain-dependent systems for meeting and email data. We find that by treating meetings and emails as conversations with general conversational features in common, we can achieve competitive results with state-of-theart systems that rely on more domain-specific features.

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Section: Email Summarizationmentioning

confidence: 99%

Summarizing spoken and written conversations

Murray

Carenini

2008

Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08

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“…Muresan et al [2] and Tzoukermann et al [3] have applied the same approach to summarize emails, namely a combination of linguistic and machine learning techniques. The paper shows that linguistic techniques and machine learning can extract high quality noun phrases for purposes of providing a summary of email messages.…”

Section: Literature Reviewmentioning

confidence: 99%

You’ve Got Email: A Workflow Management Extraction System

Chaipornkaew

Prexawanprasut

McAleer

2017

J. Rev. Global Econ.

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Email is one of the most powerful tools for communication. Many businesses use email as the main channel for communication, so it is possible that substantial data are included in email content. In order to help businesses grow faster, a workflow management system may be required. The data gathered from email content might be a robust source for a workflow management system. This research proposes an email extraction system to extract data from any incoming emails into suitable database fields. The database, which is created by the program, has been planned for the implementation of a workflow management system. The research is presented in three phases: (1) define suitable criteria to extract data; (2) implement a program to extract data, and store them in a database; and (3) implement a program for validating data in a database. Four criteria are applied for an email extraction system. The first criterion is to select contact information at the end of the email content; the second criterion is to select specified keywords, such as tel, email, and mobile; the third criterion is to select unique names, which start with a capital letter, such as the names of people, places, and corporates; the fourth criterion is to select special texts, such as Co. Ltd, .com, and www. The empirical results suggest that when all four criteria are considered, the accuracy of a program and percentage of blank fields are at an acceptable level compared with the results from other criteria. When four criteria are applied to extract 7,340 emails in English, the accuracy of this experiment is approximately 68.66%, while the percentage of blank fields in a database is approximately 68.05. The database created by the experiment can be applied in a workflow management system. KeywordsBusiness operations, startup business, import/export industry, email, business data, workflow management system, business transactions, migrating, email extraction system. Revised: May 2017 * The authors would like to express their gratitude to the Executive Vice-President of Finish International Freight Co. Ltd, as well as another two anonymous companies which cannot be mentioned because of confidentiality. The companies provided very useful information and insights to conduct this research. Thanks also to Khun Natthicha Phonjan and Khun Sariporn Plipon, who assisted greatly to edit and verify the accuracy of the program. It is appreciated that the business data provided by the three selected businesses are sensitive, and will not be disclosed or used for any purpose other than the research for the paper. The authors are also grateful for the helpful comments and suggestions of Chia-Lin Chang. Corresponding author: Takorn Prexawanprasut (takorn.pre@dpu.ac.th) 1 JEL Classification AbstractEmail is one of the most powerful tools for communication. Many businesses use email as the main channel for communication, so it is possible that substantial data are included in email content. In order to help businesses grow faster, a workflow management system ma...

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“…One of the first attempts that uses extraction of important phrases from emails as a way of email summarization is in [63] [64]. In [63], researchers focused on thread summarization using content and structural features to group sentences as "relevant" and "not relevant". Other researchers used a scoring-based summarization to generate "thread overviews" on mailing lists.…”

Section: Related Workmentioning

confidence: 99%

Extractive Summarization Using Structural Syntax, Term Expansion and Refinement

Elhadi¹

2017

IJIS

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This paper investigates a procedure developed and reports on experiments performed to studying the utility of applying a combined structural property of a text's sentences and term expansion using WordNet [1] and a local thesaurus [2] in the selection of the most appropriate extractive text summarization for a particular document. Sentences were tagged and normalized then subjected to the Longest Common Subsequence (LCS) algorithm [3] [4] for the selection of the most similar subset of sentences. Calculated similarity was based on LCS of pairs of sentences that make up the document. A normalized score was calculated and used to rank sentences. A selected top subset of the most similar sentences was then tokenized to produce a set of important keywords or terms. The produced terms were further expanded into two subsets using 1) WorldNet; and 2) a local electronic dictionary/thesaurus. The three sets obtained (the original and the expanded two) were then re-cycled to further refine and expand the list of selected sentences from the original document. The process was repeated a number of times in order to find the best representative set of sentences. A final set of the top (best) sentences was selected as candidate sentences for summarization. In order to verify the utility of the procedure, a number of experiments were conducted using an email corpus. The results were compared to those produced by human annotators as well as to results produced using some basic sentences similarity calculation method. Produced results were very encouraging and compared well to those of human annotators and Jacquard sentences similarity.

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Combining linguistic and machine learning techniques for email summarization

Cited by 38 publications

References 13 publications

Summarizing spoken and written conversations

Summarizing spoken and written conversations

You’ve Got Email: A Workflow Management Extraction System

Extractive Summarization Using Structural Syntax, Term Expansion and Refinement

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