Name
Abstract | ||
|---|---|---|
Extractive summarization techniques typically aim to maximize the information coverage of the summary with respect to the original corpus and report accuracies in ROUGE scores. Automated text summarization techniques should consider the dimensions of comprehensibility, coherence and readability. In the current work, we identify the discourse structure which provides the context for the creation of a sentence. We leverage the information from the structure to frame a monotone (non-decreasing) sub-modular scoring function for generating comprehensible summaries. Our approach improves the overall quality of comprehensibility of the summary in terms of human evaluation and gives sufficient content coverage with comparable ROUGE score. We also formulate a metric to measure summary comprehensibility in terms of Contextual Independence of a sentence. The metric is shown to be representative of human judgement of text comprehensibility. |
Year | Venue | Field |
|---|---|---|
2016 | SRW@HLT-NAACL | Modular form,Information coverage,Automatic summarization,Computer science,Judgement,Readability,Natural language processing,Artificial intelligence,Sentence,Monotone polygon,Discourse structure |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Litton J. Kurisinkel | 1 | 1 | 1.70 |
| Pruthwik Mishra | 2 | 3 | 5.12 |
| Vigneshwaran Muralidaran | 3 | 1 | 1.36 |
| Vasudeva Varma | 4 | 640 | 95.84 |
| Dipti Misra Sharma | 5 | 262 | 45.90 |