| Title |
Textual Distraction as a Basis for Evaluating Automatic Summarisers |
| Author(s) |
Antoinette Renouf, Andrew Kehoe RDUES, University of Liverpool |
| Session |
P16-E |
| Abstract |
Our summarisation tool, SEAGULL (Summary Extraction Algorithm Generated Using Lexical Links), is a sentence extractor which exploits the patterns of lexical repetition across a text and creates abridgements which express non-trivially the conceptual content and development of topic. In this paper, we report on a test devised to assess its performance against other summarisers. This involves the introduction of progressive batches of unrelated sentences into a source text. Targeted distraction reveals the relative degrees of robustness in summariser performance. The tests show that our system functions best. |
| Keyword(s) |
summarisation, summarization, abridgement, lexical cohesion, repetition, topic, aboutness, textual distraction, extraction, evaluation |
| Language(s) | English |
| Full Paper |