Print

2017　Advanced Topics in Computing AO

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
School of Computing
Instructor(s)
Teufel Simone 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Tue1-2(W831)  Fri1-2(W831)  
Group
-
Course number
XCO.T497
Credits
2
Academic year
2017
Offered quarter
2Q
Syllabus updated
2017/4/19
Lecture notes updated
2017/8/1
Language used
English
Access Index

Course description and aims

This course provides an overview of the area of automatic text summarisation. Summarisation is the process of shortening a text to its core information content. It is often seen as a general task to assess the level of text understanding achieved by a method, but the task is also very practically usable. The course assumes general knowledge of natural language processing. It enables students to understand research papers in the area of summarisation and to start designing their own summarisation-based research and implementation based on the information given in the course. Careful and objective evaluation of summarisation is particularly important, given the vague definition of the task. The course therefore pays special attention to evaluation. The class is a mixture of lectures, student presentations, and discussion of research methods. Class size is restricted to 10 students.

Student learning outcomes

At the end of the course students should be able to
- digest and understand research papers on summarisation;
- explain main methods of summarisation and their challenges;
- understand evaluation methods for summarisation;
- build sample applications exemplifying the summarisers presented.
Specialist skills will be developed. Communication skills will be developed as well, because the course language is English and because student presentations are part of the assessment. Critical skills will also be developed, as there is no textbook, but research articles are given to students to deliver the background reading.

Keywords

text summarisation, extractive summarisation, noisy channel model, integer linear programming, cohesion-based method, template-based method

Competencies that will be developed

✔ Specialist skills ✔ Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Specified in the class

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction and Task Definition Specified in the class
Class 2 Importance Indicators
Class 3 Extractive Summarisation (Kupiec) and MMR
Class 4 Summarisation by TextRank
Class 5 Sentence Compression by Noisy Channel Model
Class 6 Summarisation by Integer Linear Programming
Class 7 Extraction-Template Based Summarisation
Class 8 Cohesion-based Summarisation
Class 9 Narrative Summarisation
Class 10 Story Understanding and Summarisation
Class 11 Memory-limitation Summarisation
Class 12 Scientific Summarisation
Class 13 Intrinsic and Extrinsic Summary Evaluation
Class 14 Evaluation based on Meaning units
Class 15 Wrap-up and Outlook

Textbook(s)

None. Reading background (papers) will be announced ahead of each lecture on the website.

Reference books, course materials, etc.

* Inderjeet Mani. Automatic Text Summarisation. 2001. John Benjamins Publishing.
* Juan-Manuel Torres-Moreno (editor). Automatic Text Summarisation. 2014. Wiley.
Note -- I will not follow these text books but use original materials (research papers).
Please don't buy these books; they are listed here just for your information.

Assessment criteria and methods

10 % Contribution to Class discussion.
20 % Presentation.
70 % Literature-based Essay or Report on Student-Designed Summarisation Experiment
(to be discussed with lecturer beforehand)

Related courses

  • ART.T459 ： Natural Language Processing

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Programming Ability
Completion of Course "Natural Language Processing" is preferable.

Page Top