This paper proposes an approach for automatically evaluating summaries based on Semantic Link Network (SLN). Three factors about the quality of summary are taken into account: 1) Fidelity, inspecting whether a summary conveys the core themes of the source text; 2) Conciseness, inspecting the non-redundancy between the sentences of a summary; and, 3) Coherence, inspecting the relevance among all the themes contained in a summary. A summary gets a quality rating based on its performance on the three factors. Experimental results show that the quality ratings of summaries given by our approach are close to the results of manual evaluation.
|Name||Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019|
|Conference||2019 15th International Conference on Semantics, Knowledge and Grids (SKG)|
|Period||17/09/19 → 18/09/19|
- Automatic text summarization
- Semantic link network
- Summarization evaluation
- Textual coherence