Multiple Alternative Sentence Compressions and Word-Pair Antonymy for Automatic Text Summarization and Recognizing Textual Entailment

The University of Maryland participated in three tasks organized by the Text Analysis Conference 2008 (TAC 2008): (1) the update task of text summarization; (2) the opinion task of text summarization; and (3) recognizing textual entailment (RTE). At the heart of our summarization system is Trimmer, which generates multiple alternative compressed versions of the source sentences that act as candidate sentences for inclusion in the summary. For the first time, we investigated the use of automatically generated antonym pairs for both text summarization and recognizing textual entailment. The UMD summaries for the opinion task were especially effective in providing non-redundant information (rank 3 out of a total 19 submissions). More coherent summaries resulted when using the antonymy feature as compared to when not using it. On the RTE task, even when using only automatically generated antonyms the system performed as well as when using a manually compiled list of antonyms.