| 研究生: |
許逸翔 HSU, I-HSIANG |
|---|---|
| 論文名稱: |
基於PREAFS 的樹狀知識圖譜之多文件懶人包 Multi-Documents Guidance Summary based on PREAFS-Tree-Structured Knowledge Graph |
| 指導教授: |
盧文祥
Lu, Wen-Hsiang |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering |
| 論文出版年: | 2023 |
| 畢業學年度: | 111 |
| 語文別: | 英文 |
| 論文頁數: | 47 |
| 中文關鍵詞: | 多文章摘要 、知識圖譜 、懶人包系統 、語意概念設計 、語意概念抽取 |
| 外文關鍵詞: | Multi-Documents Summarization, Knowledge Graph, Guidance Summary System, Concept Pattern Design, Concept Pattern Extraction |
| 相關次數: | 點閱:54 下載:0 |
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隨著網路的蓬勃發展,存在於網路上的資訊日漸遽增,人們逐漸習慣以網路作為獲
取相關知識的途徑,其中也包括閱讀新聞了解時事。然而大量的文章數目與零碎的
資訊結構,造成使用者閱讀上的負擔。
為了解決上路所遇到的問題,許多研究進行抽取式或生成式的摘要,但都會面臨摘
要出的句子並無法闡述事情的起承轉合,仍然需要閱讀多篇新聞,來補足不同的面
相,因此本論文將提出摘要技術與樹狀知識圖譜整合的系統。
在本論文中,首先利用時間維度尋找當前熱門的議題,並將相關的新聞導入開源的
語言工具,用文法分析樹抽取出「主詞片語- 動詞片語- 受詞片語」的候選摘要句,
同時搭配新聞中的人物、時間、地點補充結構訊息。接著運用提前設計好的概念結
構將候選摘要句進行6 大面相分類,分別是「前提、原因、事件本身、影響、未來
方向、實際策略」,最後將候選摘句串聯成完整摘要,並建構成知識圖譜,形成懶
人包,方便使用者可以藉由本系統所提供的介面,快速釐清事情的始末並理解其內
容。
With the booming development of the Internet, the amount of information available online has rapidly increased. People are gradually accustomed to using the Internet to stay informed about current events. However, the large number of articles and fragmented information structure impose a burden on users' reading experience.
To address the challenges encountered in this context, many studies have focused on extractive or generative summarization techniques. However, the summaries generated often fail to provide a comprehensive narrative or account of the events, requiring users to read multiple news articles to gather different perspectives. Therefore, this paper proposes a system that integrates summarization techniques with a tree-structured knowledge graph.
In this paper, we first use the temporal dimension to identify current trending topics and import relevant news articles into an open-source language tool. We extract candidate summaries using syntactic parsing trees in the form of "Subject phrase - Verb phrase - Object phrase." Additionally, we supplement the structural information with entities such as people, time, and locations found in the news articles. We then classify the candidate summaries into six major aspects using pre-designed concept patterns: "Premise, Reason, Event, Affect, Future Direction, Practical Strategies". Finally, the candidate summaries are concatenated into a guidance summary, enhancing the coherence of the sentences, and constructing a knowledge graph for a comprehensive understanding. This system provides users with an interface to quickly grasp the essence of the events and comprehend their content.
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校內:2028-07-31公開