Dependency-based Sentence Alignment for Multiple Document Summarization

Dependency-based Sentence Alignment for Multiple Document Summarization Tsutomu HIRAO and Jun SUZUKI and Hideki ISOZAKI and Eisaku MAEDA NTT Communication Science Laboratories, NTT Corp. 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237 Japan � hirao,jun,isozaki,maeda � @cslab.kecl.ntt.co.jp Abstract In this paper, we describe a method of automatic sentence alignment for building extracts from ab- stracts in automatic summarization research. Our method is based on two steps. First, we introduce the “dependency tree path” (DTP). Next, we calcu- late the similarity between DTPs based on the ESK (Extended String Subsequence Kernel), which con- siders sequential patterns. By using these proce- dures, we can derive one-to-many or many-to-one correspondences among sentences. Experiments us- ing different similarity measures show that DTP consistently improves the alignment accuracy and that ESK gives the best performance. 1 Introduction Many researchers who study automatic summariza- tion want to create systems that generate abstracts of documents rather than extracts. We can gener- ate an abstract by utilizing various methods, such as sentence compaction, sentence combination, and paraphrasing. In order to implement and evalu- ate these techniques, we need large-scale corpora in which the original sentences are aligned with summary sentences. These corpora are useful for training and evaluating sentence extraction systems. However, it is costly to create these corpora. Figure 1 shows an example of summary sentences and original sentences from TSC-2 (Text Summa- rization Challenge 2) multiple document summa- rization data (Okumura et al., 2003). From this ex- ample, we can see many-to-many correspondences. For instance, summary sentence (A) consists of a part of source sentence (A). Summary sentence (B) consists of parts of source sentences (A), (B), and (C). It is clear that the correspondence among the sentences is very complex. Therefore, robust and accurate alignment is essential. In order to achieve such alignment, we need not only syntactic information but also semantic infor- mation. Therefore, we combine two methods. First, we introduce the “dependency tree path” (DTP) for Source(A): ���� �     ! "!#�$ �%�&' (*)+-, . / 0 1 2 3 4 5 0 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q L 0 1 2 3 R S T U V W X Y Z []\�_^_`ba c d e f T g hij�klnmpo!qrmtsvuw* x y zb {�|�}-~€ _‚ƒ „ *† ‡ W X ˆ ‰ Š 9 NŒ‹ + Ž First, we stop the new investment of 64-Mega bit memory from competitive companies, such as in Korea or Taiwan, and we begin the investment for development of valuable system-on-chip or 256-Mega bit DRAM from now on. Source(B): �*‘�*’ “ ” ˆ • –�— ˜�™_šœ›ž�Ÿ ¡ ¢ £ H ¤ T ¥ > • ¦ R £ : ; < = ™_š €§ v¨ ©ª \ ‹ +b¬«�­¯®-Ž On a long-term target, we plan to reduce the rate of general-purpose semiconductor enterprises that pro- duce DRAM for personal computers. Source(C): ^�`*�±°²_ : ; …

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