- 開講元
- 情報理工学院
- 担当教員名
- TEUFEL SIMONE HEIDI
- 授業形態
- 講義
- メディア利用科目
- 曜日・時限(講義室)
- 火1-2(W831) 金1-2(W831)
- クラス
- -
- 科目コード
- XCO.T497
- 単位数
- 2
- 開講年度
- 2017年度
- 開講クォーター
- 2Q
- シラバス更新日
- 2017年4月19日
- 講義資料更新日
- 2017年8月1日
- 使用言語
- 英語
- アクセスランキング
-
media
講義の概要とねらい
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.
到達目標
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.
キーワード
text summarisation, extractive summarisation, noisy channel model, integer linear programming, cohesion-based method, template-based method
学生が身につける力(ディグリー・ポリシー)
| ✔ 専門力 | ✔ 教養力 | コミュニケーション力 | 展開力(探究力又は設定力) | 展開力(実践力又は解決力) |
授業の進め方
Specified in the class
授業計画・課題
| 授業計画 | 課題 | |
|---|---|---|
| 第1回 | Introduction and Task Definition | 講義において指定する |
| 第2回 | Importance Indicators | |
| 第3回 | Extractive Summarisation (Kupiec) and MMR | |
| 第4回 | Summarisation by TextRank | |
| 第5回 | Sentence Compression by Noisy Channel Model | |
| 第6回 | Summarisation by Integer Linear Programming | |
| 第7回 | Extraction-Template Based Summarisation | |
| 第8回 | Cohesion-based Summarisation | |
| 第9回 | Narrative Summarisation | |
| 第10回 | Story Understanding and Summarisation | |
| 第11回 | Memory-limitation Summarisation | |
| 第12回 | Scientific Summarisation | |
| 第13回 | Intrinsic and Extrinsic Summary Evaluation | |
| 第14回 | Evaluation based on Meaning units | |
| 第15回 | Wrap-up and Outlook |
教科書
None. Reading background (papers) will be announced ahead of each lecture on the website.
参考書、講義資料等
* 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.
成績評価の基準及び方法
10 % Contribution to Class discussion.
20 % Presentation.
70 % Literature-based Essay or Report on Student-Designed Summarisation Experiment
(to be discussed with lecturer beforehand)
関連する科目
- ART.T459 ： 自然言語処理
履修の条件(知識・技能・履修済科目等)
Programming Ability
Completion of Course "Natural Language Processing" is preferable.
