Improving Multilingual Summarization: Using Redundancy in the Input to Correct MT errors

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 33–40, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Improving Multilingual Summarization: Using Redundancy in the Input to Correct MT errors Advaith Siddharthan and Kathleen McKeown Columbia University Computer Science Department 1214 Amsterdam Avenue, New York, NY 10027, USA. � as372,kathy � @cs.columbia.edu Abstract In this paper, we use the information re- dundancy in multilingual input to correct errors in machine translation and thus im- prove the quality of multilingual sum- maries. We consider the case of multi- document summarization, where the input documents are in Arabic, and the output summary is in English. Typically, infor- mation that makes it to a summary appears in many different lexical-syntactic forms in the input documents. Further, the use of multiple machine translation systems pro- vides yet more redundancy, yielding dif- ferent ways to realize that information in English. We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identify- ing and generating from such redundancy, focusing on noun phrases. 1 Introduction Multilingual summarization is a relatively nascent research area which has, to date, been addressed through adaptation of existing extractive English document summarizers. Some systems (e.g. SUM- MARIST (Hovy and Lin, 1999)) extract sentences from documents in a variety of languages, and trans- late the resulting summary. Other systems (e.g. Newsblaster (Blair-Goldensohn et al., 2004)) per- form translation before sentence extraction. Read- ability is a major issue for these extractive systems. The output of machine translation software is usu- ally errorful, especially so for language pairs such as Chinese or Arabic and English. The ungrammati- cality and inappropriate word choices resulting from the use of MT systems leads to machine summaries that are difficult to read. Multi-document summarization, however, has in- formation available that was not available during the translation process and which can be used to im- prove summary quality. A multi-document summa- rizer is given a set of documents on the same event or topic. This set provides redundancy; for example, each document may refer to the same entity, some- times in different ways. It is possible that by ex- amining many translations of references to the same entity, a system can gather enough accurate informa- tion to improve the translated reference in the sum- mary. Further, as a summary is short and serves as a surrogate for a large set of documents, it is worth investing more resources in its translation; readable summaries can help end users decide which docu- ments they want to spend time deciphering. Current extractive approaches to summarization are limited in the extent to which they address qual- ity issues when the input is noisy. Some new sys- tems attempt substituting sentences or clauses in the summary with similar text from extraneous but topic related English documents (Blair-Goldensohn et al., 2004). This improves readability, but can only be used in limited circumstances, in order to avoid substituting an English sentence that …

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