Sentence Compression as a Component of a Multi-Document Summarization System

We applied a single-document sentencetrimming approach (Trimmer) to the problem of multi-document summarization. Trimmer was designed with the intention of compressing a lead sentence into a space consisting of tens of characters. In our Multi-Document Trimmer (MDT), we use Trimmer to generate multiple trimmed candidates for each sentence. Sentence selection is used to determine which trimmed candidates provide the best combination of topic coverage and brevity. We demonstrate that we were able to port Trimmer easily to this new problem. We also show that MDT generally ranked higher for recall than for precision, suggesting that MDT is currently more successful at finding relevant content than it is at weeding out irrelevant content. Finally, we present an error analysis that shows that, while sentence compressions is making space for additional sentences, more work is needed in the area of generating and selecting the right candidates.