<figure><img src="image_1.png" /></figure>

HAL
open science

# Lessons Learned From Research on Multimedia Summarization

Guillaume Touya

<figure><img src="image_2.png" /></figure>

## To cite this version:

Guillaume Touya. Lessons Learned From Research on Multimedia Summarization. Proceedings of 18th ICA Workshop on Generalisation and Multiple Representation, ICA, Aug 2015, Rio de Janeiro, Brazil. hal-02274443

HAL Id: hal-02274443

https://hal.science/hal-02274443

Submitted on 29 Aug 2019

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_3.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Lessons Learned From Research on Multimedia Summarization

Guillaume Touya
IGN France - COGIT

73 avenue de Paris 94165 Saint-Mandé

firstname.lastname@ign.fr

## 1 Introduction

Map generalization is often considered as a cognitive task similar to text summary. Like text summary, generalization seeks to reduce the level of detail of initial data, highlighting the important features regarding a given need, and preserving the main characteristics of the initial data (Ruas 2002). The automation of text summarization is key research topic for language processing scientists, and it should be interesting to verify if the similarities in the human cognitive process lead to similarities in the automation techniques. Moreover, text summarization is a part of the multimedia summarization problem that also includes video and music summarization, and this complete literature is interesting to review. The aim of the paper is also to identify some guidelines for further map generalization research that can be derived from the multimedia summarization research community.

This paper first identifies similarities and differences in both automation problems. Then, section 3 proposes ideas from multimedia summarization that could be beneficial for the map generalization community. Finally, the paper is concluded with some ideas for further research opportunities.

## 2 Similarities and Differences between Both Problems

Exhaustive reviews of techniques have been published for text summarization (Mani 1999, Das & Martins 2007), video summarization (Truong & Venkatesh 2007), or music summarization (Peeters 2004, Jun & Hwang 2013), to go into more details. Some of these techniques are presented here only to illustrate the similarities and differences between summarization and generalization.

First, map generalization and text summarization are complex cognitive tasks that do not have any exact perfect solution. Different human cartographers may create different generalized maps from the same geographic data that can be considered as good. Similarly, human summarizers do not agree with each other with a same document and same rules to guide them. In both cases, it has been noted that the lack of metrics to globally evaluate a solution illustrates the difficulty of the task (Das & Martins 2007, Stöter et al 2014).

Most summarization techniques are based on the selection of the most important sentences in the document, which are extracted to create the summary. For instance, the seminal proposition from Luhn (1958) analysed the frequency of words and the position of sentences to extract the key sentences (Figure 1). The same mechanisms clearly drive the selection
operation in map generalization (McMaster & Shea 1992). So we should be able to use in selection processes some methods defined for text extraction.

<figure><img src="image_4.png" /><figcaption>Figure 1. Example of sentence extraction to derive an abstract from a newspaper article (Luhn 1958).</figcaption></figure>

Furthermore, context is fundamental in both map generalization and summarization, as it helps brain to build the comprehension of the map or the text. An isolated building and a building in a dense city are not considered similarly in generalization, and in the sentence below, only the context of the preceding sentence helps to understand what "the hat" is (MCKeown & Radev 1995).

Bob got a new Stetson. He loves the hat.
Enriching the initial cartographic data with implicit structures and patterns is essential in generalization in order to preserve or abstract the structures in the generalization process (Mackaness & Edwards 2002). Text summarization has the same requirements, has grammatical structure is a key to text meaning. Thus, some text summarization methods first analyse the grammatical structures of sentences (Figure 2) before abstracting the document into a summary (McKeown *et al* 1999).

<figure><img src="image_5.png" /><figcaption>Figure 2. Dependency tree structuring the sentence "McVeigh, 27, was charged with the bombing", example from (McKeown *et al* 1999).</figcaption></figure>

Other methods first classify the document to summarize into well-known sequences, such as verses and choruses in songs (Figure 3), prior to the summarization. Then, for instance, only one verse and one chorus are kept (Peeters 2004). For instance, this can be seen as analogous to the classification of urban blocks in (Trévisan & Gaffuri 2004) in order to use different AGENT parameters for each type of block.

<figure><img src="image_6.png" /><figcaption>Figure 3. Characteristic sequences in the song "Smell Like Teen Spirit" from Nirvana, detected by the Peeters algorithm (2004).</figcaption></figure>
Added to that, many summarization methods use multiple document as input, for instance to summarize news using all the existing news channels, and their different point of view on an event. This process can be seen as similar to the conflation process required to make mashup maps out of several sources of geographical data.

Despite these similarities, the automatic processes of map generalization and multimedia summarization are very different. The main difference lies in the nature of the input data. Vector geographical data requires computational geometry techniques to deal with two or three dimensions data, while text (one dimension) requires natural language processes and videos require image processing techniques.

The need for generalization mainly derives from the legibility, or eye perception problems caused by scale reduction, so the generalization processes mainly seek to derive legible maps, where eyes are able to distinguish all details; keeping the main features of the map is only one of the objectives. Text summary has no such constraint, and only aims at the optimal number of words to grasp the meaning of a text. So, the multimedia summarization processes can only be compared to the selection processes in map generalization.

Finally, generalization seeks to convey more the geography behind the data, and its implicit spatial relations and structures, than to convey the precise positioning of objects. As a consequence, exaggeration operations (McMaster & Shea 1992) such as typification, dilatation, or parallelism enhancement are quite common in generalization (Figure 4), while summaries remain faithful to the original source data. Nevertheless, this difference is slightly modified when text summary is less literal, and seeks to highlight the important aspects of a text like map caricature (McKeown *et al* 1999).

<figure><img src="image_7.png" /><figcaption>Figure 4. Examples of caricature and exaggeration operations in generalization that cannot be related to summarization operations.</figcaption></figure>

# 3 What Can Be Learned for Map Generalization Research

This short review and the differences highlighted in section 2 show that multimedia summarization techniques cannot directly be applied to map generalization. However, some lessons can be learned from this research domain, and some are presented in this section.

## 3.1 A Massive Use of Machine Learning

Although the machine learning techniques have been tested to automate generalization (e.g. Weibel et al 1995, Mustière et al 2000, Kilpelainen 2000, Burghardt & Neun 2006), it appears that this technique has been under-used when compared to the diversity of learning techniques used in text summarization (Das & Martins 2007).

For instance, the orchestration and the parametrization of generalization processes is one of the main difficulties remaining in generalization research, but is it possible to learn in already generalized maps how to guide the orchestration, or the parametrization of algorithms? Taillandier et al (2011) used learning similarly to optimize the parametrization of
the AGENT generalization model, but this could be made in a broader way. I believe that the
community needs to look once again at the machine learning research, to see if their recent
outcomes could help us (e.g. LeCun et al 2015).

## 3.2 The Notions of Importance and Redundancy

The parallel between text extraction and the selection operation highlighted in section 2 make the criteria used in text extraction interesting to study. Importance and redundancy are two key notions in the text extraction methods. Several proposals exist to infer importance and redundancy of words or expressions, and could be transferred through analogies to map generalization. Mackaness and Gould (2014) pleaded for a better consideration of geographic saliency in map generalization, and such analogies could help us to do so.

Road selection processes are an interesting case study for highlighting the usefulness of the notion of redundancy. Most main contributions in the domain tried to identify the main roads with graph theory based methods, and/or Gestalt based methods, and provide quite satisfying results. But thinking the other way round, i.e. removing the roads identified as redundant, would maybe improve the existing methods (Figure 5).

<figure><img src="image_8.png" /><figcaption>Figure 5. Three redundant roads in a network as they provide neither shortcut nor additional connectivity.</figcaption></figure>

## 3.3 A Major Focus on Evaluation

The generalization research community has clearly neglected the evaluation step compared to the number of papers regarding algorithms for instance, and recent papers highlighted this lack of major contribution (Stöter et al 2014, Mackaness & Gould 2014). On the contrary, multimedia summarization research significantly focused on evaluation protocols, with standards for manual and automatic evaluation, and many metrics to evaluate a summary and compare alternative methods (Lin 2004). The agreement on standards to evaluate a text summary in comparison to one or several references greatly helped the development of automatic summarization techniques. I believe that the research presented in Stöter et al (2014) should pushed further and that the community should make similar agreements for standardizing the evaluation of automatic generalization, with for instance a standard set of constraints to satisfy to maximize legibility and a set of metrics to assess global readability.
## 3.4 Benchmarks to Allow Reproducible Science

Reproducible science is a key factor of scientific thinking (Peng 2011), as it allows scientists to compare each other methods with reproducible experiments. In map generalization, sharing algorithms in a web platform, such as the web services framework proposed in (Foerster et al 2008), would help comparing algorithms to each other.

In order to promote reproducible science, to compare the large number of proposed methods, and also to provide datasets to academic researchers, the text summarization community soon organized workshops to develop evaluation competitions (Das & Martins 2007). TREC<fnref n="1" />, DUC<fnref n="2" /> and MUC<fnref n="3" /> promoted evaluation baselines on chosen training datasets. For instance, guidelines for manual evaluation of summaries have been defined with such initiatives (Lin & Hovy 2002), and the same could be made for generalization benefiting from the knowledge of the cartographers working in the national mapping agencies. The need for such standard has been recently acknowledged by Stöter et al (2014).

As mentioned earlier, such as generalization, there is not a unique good solution to multimedia summarization problems, but several. Text summarization benchmarks evolved to propose now several acceptable summaries for training texts, and this diversity of solutions is used by evaluation systems (Lin 2004).

The EuroSDR project on the state-of-the-art of commercial software in generalization was a first step to the creation of benchmark datasets with sets of constraints related to each of the four datasets (Stöter et al 2010). We should now go further by providing open datasets with sets of constraints to satisfy, good results, and existing processes to compare to. The recent research on sharing the generalization knowledge in ontologies (Gould et al 2014) is also a step forward.

# 4 Conclusion

Although texts and maps are different kinds of information, the cognitive processes of summarization and generalization are similar. So it is interesting for the generalization research community to learn from the research on text/multimedia summarization. The main feedbacks are the under-use of machine learning in generalization, the lack for importance, saliency and redundancy definitions, the lack of focus on generalization evaluation, and finally the importance of benchmarks to allow reproducible research. Although the techniques used in multimedia summarization cannot be directly used in map generalization, I believe that generalization researchers should regularly review the summarization community outcomes in order to integrate major trends.

More generally, the effort presented in this paper of reviewing a new field of science and relating it to map generalization could be done for other interesting domains. For instance, the research on visual search, tries to understand the mechanics the brain uses to optimize visual search (see Eckstein 2011 for a review). It would be useful to understand how the brain searches information in a map to better generalize it.

<footnote n="1">¹ http://trec.nist.gov</footnote>

<footnote n="2">² http://duc.nist.gov</footnote>

<footnote n="3">³ http://www-nlpir.nist.gov/relatedProjects/muc/proceedings/muc_7_toc.html</footnote>
# Acknowledgments

I would like to thank the anonymous reviewer for his/her valuable comments.

# References

Burghardt, D. and M. Neun (2006). Automated sequencing of generalisation services based on collaborative filtering. In M. Raubal, H. J. Miller, A. U. Frank, and M. F. Goodchild (Eds.), *Geographic Information Science - 4th International Conference GIScience*, IFGI prints, pp. 41-46.
Münster, Germany: IFGI prints.

Das, D. and A. F. T. Martins (2007). A survey on automatic text summarization. *Literature Survey for the Language and Statistics II course at CMU*, 4, 192-195.

Eckstein, M. P. (2011). Visual search: A retrospective. *Journal of Vision* 11 (5), 14.

Foerster, T., D. Burghardt, M. Neun, N. Regnauld, J. Swan, and R. Weibel (2008). Towards an interoperable web generalisation services framework – current work in progress. In *Proceedings of 11th ICA Workshop on Generalisation and Multiple Representation*, Montpellier, France.

Gaffuri, J. and J. Trévisan (2004). Role of urban patterns for building generalisation: An application of AGENT. In *ICA Workshop on Generalisation and Multiple representation*, commission on map generalisation.

Jun, S. and E. Hwang (2013). Music segmentation and summarization based on self-similarity matrix. In *Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, ICUIMC '13*, New York, NY, USA. ACM.

Kilpelainen, T. (2000). Knowledge acquisition for generalization rules. *Cartography and Geographic Information Science* 27 (1), 41-50.

LeCun, Y., Bengio, Y., and G. Hinton (2015). Deep learning. *Nature* 521 (7553), 436-444.

Lin, C.-Y. (2004). Rouge: A package for automatic evaluation of summaries. *Text Summarization Branches Out: Proceedings of the ACL-04 Workshop*, 74-81.

Lin, C. Y. and E. Hovy (2002). Manual and automatic evaluation of summaries. In *Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4, AS '02*, Stroudsburg, PA, USA, pp. 45-51. Association for Computational Linguistics.

Luhn, H. P. (1958). The automatic creation of literature abstracts. *IBM Journal of Research and Development* 2 (2), 159-165.

Mackaness, W. A. and G. Edwards (2002). The importance of modelling pattern and structure in automated map generalisation. In *Proceedings of the Joint ISPRS/ICA Workshop on Multi-Scale Representations of Spatial Data*, pp. 7-8.

Mackaness, W. A. and N. M. Gould (2014). The role of geography in automated generalisation. In *Proceedings of 17th ICA Workshop on Generalisation and Multiple Representation*, Vienna, Austria.

Mani, I. (1999). *Advances in Automatic Text Summarization*. Cambridge, MA, USA: MIT Press.

McKeown, K. and D. R. Radev (1995). Generating summaries of multiple news articles. In *Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval*, SIGIR '95, New York, NY, USA, pp. 74-82. ACM.

McKeown, K. R., J. L. Klavans, V. Hatzivassiloglou, R. Barzilay, and E. Eskin (1999). Towards multidocument summarization by reformulation: Progress and prospects. In *Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence*, AAAI
'99/IAAI '99, Menlo Park, CA, USA, pp. 453-460. American Association for Artificial Intelligence.

McMaster, R. and K. S. Shea (1992). *Generalization in Digital Cartography*. Association of American Geographers Press.

Mustière, S., J.-D. Zucker, and L. Saitta (2000). Abstraction-Based machine learning approach to cartographic generalisation. In *Proceedings of 9th International Symposium on Spatial Data Handling*, Volume 1a, Beijing, China, pp. 50-63.

Peeters, G. (2004). Deriving musical structures from signal analysis for music audio summary generation: "sequence" and "state" approach. In U. Wiil (Ed.), *Computer Music Modeling and Retrieval*, Volume 2771 of *Lecture Notes in Computer Science*, pp. 143-166. Springer Berlin Heidelberg.

Peng, R. D. (2011). Reproducible research in computational science. *Science* 334 (6060), 1226-1227.

Radev, D. R., E. Hovy, and K. McKeown (2002). Introduction to the special issue on summarization. *Comput. Linguist.* 28 (4), 399-408.

Ruas, A. (2002). *Généralisation et représentation multiple*. Traité IGAT. Lavoisier.

Stoter, J., D. Burghardt, C. Duchêne, B. Baella, N. Bakker, C. Blok, M. Pla, N. Regnauld, G. Touya, and S. Schmid (2009). Methodology for evaluating automated map generalization in commercial software. *Computers, Environment and Urban Systems* 33 (5), 311-324.

Stoter, J., X. Zhang, H. Stigmar, and L. Harrie (2014). Evaluation in generalisation. In D. Burghardt, C. Duchêne, and W. Mackaness (Eds.), *Abstracting Geographic Information in a Data Rich World*, Lecture Notes in Geoinformation and Cartography, pp. 259-297. Springer International Publishing.

Taillandier, P., C. Duchêne, and A. Drogoul (2011, November). Automatic revision of rules used to guide the generalisation process in systems based on a trial and error strategy. *International Journal of Geographical Information Science* 25 (12), 1971-1999.

Truong, B. T. and S. Venkatesh (2007). Video abstraction: A systematic review and classification. *ACM Trans. Multimedia Comput. Commun. Appl.* 3 (1).

Weibel, R., S. Keller, and T. Reichenbacher (1995). Overcoming the knowledge acquisition bottleneck in map generalization: The role of interactive systems and computational intelligence. In A. U. Frank and W. Kuhn (Eds.), *Spatial Information Theory A Theoretical Basis for GIS*, Volume 988 of Lecture Notes in Computer Science, pp. 139-156. Berlin Heidelberg: Springer.