Generation of Textual Description

Project Timeline & Milestones

Timeframe	Task	Completion
Week 4	Exploring the dataset Exploring in-context learning models for text summarization	✓
Week 5	Identify patterns and edge cases from the dataset (e.g missing fields, "odd" values) Define different summarization formats accordingly to be used for in-context learning Explore the connection between the Catastici and Sommarioni dataset	✓
Week 6	Refine summarization formats Construct a pipeline connecting translation generation, summarization and validation	✓
Week 7	Evaluate summarization results
Week 8	Prepare for mid-term presentation
Week 9	Explore father-son relationship among Catastici and Sommarioni dataset
Week 10	Standardization of monthly rent column
Week 11	Verified and refined standardized rent values
Week 12	Added district mean rent column and integrated additional information into the data Refine text prompts to incorporate new information Implement evaluation methods for the final generated text
Week 13	Refine evaluation methods Split evaluation tasks among members
Week 14	Verify and combine evaluation tasks Finalize Wikipage and prepare for presentation

Methodology

Functionality of Parcels

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Standardization of Monthly Rent

Exploring Value Formats

Inferring accurate values from rent data can offer valuable insights into a parcel's history, such as its price in relation to its neighborhood, which may reflect its relative status at that time. Among 35,946 data rows, we identified that only 28,610 (~80%) contained numeric values, with the remaining entries consisting of null values or text data.

Upon exploration, we encountered examples of text data such as:

'10 ducati, 19 grossi' '40 ducati e 14 grossi' 'casa in soler'
'libertà di traghetto' '20 lire' '26 lire' '15 lire'...

These examples illustrate the diversity of information present in the text entries, including multiple currencies, descriptive text about the parcel’s function, and potential typographical errors.

To standardize the non-numeric rent values, we developed an iterative approach. Using regular expressions (regex), we captured patterns within the data. After each iteration, we matched the patterns against the dataset, identified any emerging new patterns, and incorporated them into our existing pattern set. This process was repeated until no new patterns could be found.

In the final iteration, we identified six main patterns as follows:

Pattern Name	Example	Notes
Single currency	30 lire	with optional "de piccoli" or "di piccoli"
Dual currency	7 ducati, 18 grossi	with optional "de piccoli" or "di piccoli"
Three-part currency	10 ducati, 2 lire, 8 soldi
Fractional or "e mezzo" units	8 ducati e mezzo
Time-related mentions	al mese, ogni tre mesi, per metà
Function or Ownership	casa, bottega
Others	1[5], 1'	Unmatched with any of the above

Format Selection and Hypothesis

From the identified text formats, we decided to exclude entries related to function, ownership, or other non-monetary details due to uncertainty about their origins. These deviations may stem from transcription errors or the inclusion of special characters.

For analysis, we focused on the remaining matched patterns, which revealed a mix of currencies within the entries. We identified five currency types used:

• Ducati/ducato [basis currency]
• Lire/lira/libre
• Grossi/grosso
• Soldi

To standardize the dataset, we made the following assumptions:

1. Currency synonyms: Terms like ducati and ducato refer to the same currency, as do lire, lira, libre, and grossi/grosso.
2. Default currency: If no currency is mentioned, it defaults to ducati.
3. Irrelevant qualifiers: Phrases like de piccoli or di piccoli are ignored in numeric contexts. For example, 50 lire is treated the same as 50 lire di piccoli. Historically, "lire di piccoli" referred to a monetary unit based on the piccolo, a base coin in Venice, but not a distinct currency itself.
4. Exchange rate: Based on Wikipedia, we use the following conversion:
   1. 1 ducati = 6.2 lire = 24 grossi = 124 solid
   2. While exchange rates were historically dynamic and context-dependent, we adopted this commonly cited, publicly available rate.
5. Unmatched formats: Entries that do not align with the identified currency patterns are excluded from analysis and marked as -1 due to uncertainty (e.g., transcription errors or unclear historical context).

Standardization Strategy

We standardized all values by converting them to the smallest unit (soldi):

• Numeric values: Convert from ducati to soldi
• Non-numeric values:
  • Matched format: Standardize as below
  • Unmatched format: Ignore (no analysis)

Value Standardization Method

1. Single currency: For entries like 30 lire, convert directly using the chosen exchange rate.
2. Dual currency: For entries like 7 ducati, 18 grossi, separate the components, convert each, and combine the results.
3. Three-part currency: For entries like 10 ducati, 2 lire, 8 soldi, follow the same process as dual currency but account for the third component.
4. Fractional units: For entries like 8 ducati e mezzo or 8 e mezzo, treat e mezzo (and similar terms) as +0.5, then convert.
5. Time-related mentions:
   1. al mese = "monthly" (standard conversion).
   2. ogni tre mesi = "every three months" (divide by 3).
   3. per metà = "for half" (divide by 2; likely indicates partial or shared payment responsibility). For example, 36 lire per metà translates to 18 lire each for two parties or simply half of 36 lire.
   4. per 3 mesi = "for 3 months" (remain as is; indicates a fixed three-month payment).

This process results in a new dataset column, std_rent, which contains standardized monthly rent values in soldi. A few samples of the result is shown as below:

an_rendi	std_rent
30	3720.0
30.5 lire di picolli	189.1
7 ducati, 18 grossi di piccolo	1300.0
10.0 ducati, 2 lire, 8 soldi	1260.4
30 lire per mezzo	189.1
40 lire al mese	248.0

Adding Mean District Rent Value for Parcels

With the standardized rent values for each parcel, we extended our analysis to compare individual parcels against the mean rent value of their respective districts (sestiere).

During this process, we identified additional anomalies, such as values with more than four digits. Upon verification, these entries were recognized as transcription errors, likely due to confusion with the ID field. Such values were excluded from further analysis.

To compute the mean rent price for each district, we excluded both the invalid entries and the standardized -1 values, as defined earlier. The results are visualized in the following graph:

The mean monthly rent price of each district (sestiere)

We further visualized the mean district rent values on a map, utilizing geographic coordinates to explore spatial patterns and gain insights into neighborhood characteristics and rent distributions.

Distribution of mean monthly rent price by district (sestiere), visualized on exact coordinates

From this visualization, we can derive interesting insights about neighborhoods. For example, San Marco (SM) has the highest mean rent, likely due to its central location within the city. This information enables further enrichment for text generation, not only within individual districts but also for comparisons across districts, offering a richer understanding of the socio-economic dynamics of the city.