presenting the information. In other words, the algorithm produces a good ordering if the input articles' orderings have high agreement.

On the other hand, when analyzing Poor summaries, we observed that the input texts have very different orderings. By trying to maximize the agreement of the input texts' orderings, MO produces a new ordering that does not occur in any input text. The ordering is, therefore, not guaranteed to be acceptable. An example of a new produced ordering is given in Figure 5. The summary would be more readable if several sentences were moved around. An example of a better ordering is given in Figure 6. In this summary, the three sentences related to the fact that the subject had a sex-change operation are grouped together, while in the one produced by the majority ordering **algorithm, they are scattered**
throughout the summary.

![0_image_0.png](0_image_0.png)

This algorithm can be used to order sentences accurately if we are certain that the input texts follow similar organizations. This assumption **may hold in limited domains** where documents have a fixed organization of the information. However, in our case, the input texts we are processing do not have such regularities. **Looking at the daily statistics of** Newsblaster which collects clusters of related articles to **be synthesized into one summary,** we notice that the typical cluster size is seven. But every day there are several clusters which contain more than 20 and up to 70 articles to be summarized into single summaries6.

With such a big number of input articles, we cannot assume that they will all have similar ordering of the information. MO's performance critically depends on the agreement of orderings in the input texts; we, therefore, need an ordering strategy which can fit any input data. From here on, we will focus only on the Chronological Ordering algorithm and techniques to improve it.