Name
Abstract | ||
|---|---|---|
In our work we use an existing classifier to quantify and analyze the level of specific and general content in news documents and their human and automatic summaries. We discover that while human abstracts contain a more balanced mix of general and specific content, automatic summaries are overwhelmingly specific. We also provide an analysis of summary specificity and the summary quality scores assigned by people. We find that too much specificity could adversely affect the quality of content in the summary. Our findings give strong evidence for the need for a new task in abstractive summarization: identification and generation of general sentences. |
Year | Venue | Keywords |
|---|---|---|
2011 | Monolingual@ACL | existing classifier,automatic summary,text specificity,summary specificity,general sentence,abstractive summarization,summary quality score,specific content,general content,news summary,balanced mix,human abstract |
Field | DocType | Volume |
Automatic summarization,Information retrieval,Computer science,Natural language processing,Artificial intelligence,Classifier (linguistics) | Conference | W11-16 |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Annie Louis | 1 | 443 | 24.78 |
| Ani Nenkova | 2 | 1831 | 109.14 |